Gastos Casa
02 octubre, 2023
1. Gastos (cálculos antiguos)
Gastos_casa %>%
dplyr::select(-Tiempo,-link) %>%
dplyr::select(fecha, gasto, monto, gastador,obs) %>% tail(30) %>%
knitr::kable(format = "markdown", size=12)| fecha | gasto | monto | gastador | obs |
|---|---|---|---|---|
| 6/8/2023 | Gas | 16650 | Andrés | NA |
| 6/8/2023 | Gas | 23666 | Tami | Parafina |
| 8/8/2023 | Comida | 78577 | Tami | Supermercado |
| 9/8/2023 | Agua | 11520 | Andrés | NA |
| 15/8/2023 | Comida | 51910 | Tami | Supermercado |
| 16/8/2023 | Bencina + peajes Maite | 49000 | Tami | NA |
| 16/8/2023 | Comida | 13500 | Tami | Maitemarket |
| 20/8/2023 | VTR | 21990 | Andrés | NA |
| 21/8/2023 | Comida | 99535 | Tami | NA |
| 21/8/2023 | Comida | 27680 | Andrés | nueces almendras |
| 26/8/2023 | Comida | 71467 | Tami | Supermercado |
| 31/8/2023 | Netflix | 5940 | Tami | NA |
| 1/9/2023 | Comida | 94874 | Tami | Supermercado |
| 10/9/2023 | Comida | 85445 | Tami | Supermercado |
| 11/9/2023 | Agua | 10332 | Andrés | NA |
| 16/9/2023 | Comida | 79913 | Tami | Supermercado |
| 16/9/2023 | Enceres | 14400 | Tami | Incoludido |
| 16/9/2023 | Comida | 18580 | Andrés | Johnny Rockets |
| 16/9/2023 | Comida | 38151 | Andrés | Frutos secos |
| 16/9/2023 | Diosi | 21081 | Andrés | antiparasitario |
| 17/9/2023 | Diosi | 8000 | Andrés | arena |
| 18/9/2023 | Comida | 10000 | Andrés | empanadas (3) menos helado q me comí |
| 20/9/2023 | VTR | 21990 | Andrés | NA |
| 16/9/2023 | Comida | 27980 | Tami | Cajas Soul Bar |
| 23/9/2023 | Comida | 57639 | Tami | Supermercado |
| 24/9/2023 | Diosi | 8000 | Andrés | arena diosi 10kg |
| 30/9/2023 | Electricidad | 44407 | Andrés | NA |
| 30/9/2023 | Comida | 51726 | Tami | Supermercado |
| 31/3/2019 | Comida | 9000 | Andrés | NA |
| 8/9/2019 | Comida | 24588 | Andrés | Super Lider |
#para ver las diferencias depués de la diosi
Gastos_casa %>%
dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>%
dplyr::mutate(fecha=strftime(fecha, format = "%Y-W%V")) %>%
dplyr::mutate(gastador=ifelse(gastador=="Andrés",1,0)) %>%
dplyr::group_by(gastador, fecha,.drop = F) %>%
dplyr::summarise(gasto_media=mean(monto,na.rm=T)) %>%
dplyr::mutate(treat=ifelse(fecha>"2019-W26",1,0)) %>%
#dplyr::mutate(fecha_simp=lubridate::week(fecha)) %>%#después de diosi. Junio 24, 2019
dplyr::mutate(gastador_nombre=plyr::revalue(as.character(gastador), c("0" = "Tami", "1"="Andrés"))) %>%
assign("ts_gastos_casa_week_treat", ., envir = .GlobalEnv)
gplots::plotmeans(gasto_media ~ gastador_nombre, main="Promedio de gasto por gastador", data=ts_gastos_casa_week_treat,ylim=c(0,75000), xlab="", ylab="")
par(mfrow=c(1,2))
gplots::plotmeans(gasto_media ~ gastador_nombre, main="Antes de Diosi", data=ts_gastos_casa_week_treat[ts_gastos_casa_week_treat$treat==0,], xlab="", ylab="", ylim=c(0,70000))
gplots::plotmeans(gasto_media ~ gastador_nombre, main="Después de Diosi", data=ts_gastos_casa_week_treat[ts_gastos_casa_week_treat$treat==1,], xlab="", ylab="",ylim=c(0,70000))
library(ggiraph)
library(scales)
#if( requireNamespace("dplyr", quietly = TRUE)){
gg <- Gastos_casa %>%
dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>%
dplyr::mutate(gastador=ifelse(gastador=="Andrés",1,0)) %>%
dplyr::mutate(fecha_simp=tsibble::yearweek(fecha)) %>%
dplyr::mutate(fecha_week=strftime(fecha, format = "%Y-W%V")) %>%
dplyr::mutate(treat=ifelse(fecha_week>"2019 W26",1,0)) %>%
dplyr::mutate(gastador_nombre=plyr::revalue(as.character(gastador), c("0" = "Tami", "1"="Andrés"))) %>%
# dplyr::mutate(week=as.Date(as.character(lubridate::floor_date(fecha, "week"))))%>%
#dplyr::mutate(fecha_week= lubridate::parse_date_time(fecha_week, c("%Y-W%V"),exact=T)) %>%
dplyr::group_by(gastador_nombre, fecha_simp) %>%
dplyr::summarise(monto_total=sum(monto)) %>%
dplyr::mutate(tooltip= paste0(substr(gastador_nombre,1,1),"=",round(monto_total/1000,2))) %>%
ggplot(aes(hover_css = "fill:none;")) +#, ) +
#stat_summary(geom = "line", fun.y = median, size = 1, alpha=0.5, aes(color="blue")) +
geom_line(aes(x = fecha_simp, y = monto_total, color=as.factor(gastador_nombre)),size=1,alpha=.5) +
ggiraph::geom_point_interactive(aes(x = fecha_simp, y = monto_total, color=as.factor(gastador_nombre),tooltip=tooltip),size = 1) +
#geom_text(aes(x = fech_ing_qrt, y = perc_dup-0.05, label = paste0(n)), vjust = -1,hjust = 0, angle=45, size=3) +
# guides(color = F)+
sjPlot::theme_sjplot2() +
geom_vline(xintercept = as.Date("2019-06-24"),linetype = "dashed") +
labs(y="Gastos (en miles)",x="Semanas y Meses", subtitle="Interlineado, incorporación de la Diosi; Azul= Tami; Rojo= Andrés") + ggtitle( "Figura 4. Gastos por Gastador") +
scale_y_continuous(labels = f <- function(x) paste0(x/1000)) +
scale_color_manual(name = "Gastador", values= c("blue", "red"), labels = c("Tami", "Andrés")) +
scale_x_yearweek(date_breaks = "1 month", minor_breaks = "1 week", labels=scales::date_format("%m/%y")) +
theme(axis.text.x = element_text(vjust = 0.5,angle = 35), legend.position='bottom')+
theme(
panel.border = element_blank(),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
axis.line = element_line(colour = "black")
)
# x <- girafe(ggobj = gg)
# x <- girafe_options(x = x,
# opts_hover(css = "stroke:red;fill:orange") )
# if( interactive() ) print(x)
#}
tooltip_css <- "background-color:gray;color:white;font-style:italic;padding:10px;border-radius:10px 20px 10px 20px;"
#ggiraph(code = {print(gg)}, tooltip_extra_css = tooltip_css, tooltip_opacity = .75 )
x <- girafe(ggobj = gg)
x <- girafe_options(x,
opts_zoom(min = 1, max = 3), opts_hover(css =tooltip_css))xplot<-Gastos_casa %>%
dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>%
dplyr::mutate(fecha_week=strftime(fecha, format = "%Y-W%V")) %>%
dplyr::mutate(month=as.Date(as.character(lubridate::floor_date(fecha, "month"))))%>%
dplyr::group_by(month)%>%
dplyr::summarise(gasto_total=sum(monto)/1000) %>%
ggplot2::ggplot(aes(x = month, y = gasto_total)) +
geom_point()+
geom_line(size=1) +
sjPlot::theme_sjplot2() +
geom_vline(xintercept = as.Date("2019-06-24"),linetype = "dashed") +
geom_vline(xintercept = as.Date("2019-03-23"),linetype = "dashed", color="red") +
labs(y="Gastos (en miles)",x="Meses/Año", subtitle="Interlineado, incorporación de la Diosi") +
ggtitle( "Figura. Suma de Gastos por Mes") +
scale_x_date(breaks = "1 month", minor_breaks = "1 month", labels=scales::date_format("%m/%y")) +
theme(axis.text.x = element_text(vjust = 0.5,angle = 45))
plotly::ggplotly(plot) plot2<-Gastos_casa %>%
dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>%
dplyr::mutate(fecha_week=strftime(fecha, format = "%Y-W%V")) %>%
dplyr::mutate(day=as.Date(as.character(lubridate::floor_date(fecha, "day"))))%>%
dplyr::group_by(day)%>%
summarise(gasto_total=sum(monto)/1000) %>%
ggplot2::ggplot(aes(x = day, y = gasto_total)) +
geom_line(size=1) +
sjPlot::theme_sjplot2() +
geom_vline(xintercept = as.Date("2019-06-24"),linetype = "dashed") +
geom_vline(xintercept = as.Date("2020-03-23"),linetype = "dashed", color="red") +
labs(y="Gastos (en miles)",x="Meses/Año", subtitle="Interlineado, incorporación de la Diosi") +
ggtitle( "Figura. Suma de Gastos por Día") +
scale_x_date(breaks = "1 month", minor_breaks = "1 week", labels=scales::date_format("%m/%y")) +
theme(axis.text.x = element_text(vjust = 0.5,angle = 45))
plotly::ggplotly(plot2) tsData <- Gastos_casa %>%
dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>%
dplyr::mutate(fecha_week=strftime(fecha, format = "%Y-W%V")) %>%
dplyr::mutate(day=as.Date(as.character(lubridate::floor_date(fecha, "day"))))%>%
dplyr::group_by(day)%>%
summarise(gasto_total=sum(monto))%>%
dplyr::mutate(covid=case_when(day>as.Date("2019-06-02")~1,TRUE~0))%>%
dplyr::mutate(covid=case_when(day>as.Date("2020-03-10")~covid+1,TRUE~covid))%>%
dplyr::mutate(covid=as.factor(covid))%>%
data.frame()tsData_gastos <-ts(tsData$gasto_total, frequency=7)
mstsData_gastos <- forecast::msts(Gastos_casa$monto, seasonal.periods=c(7,30))
tsData_gastos = decompose(tsData_gastos)
#plot(tsData_Santiago, title="Descomposición del número de casos confirmados para Santiago")
forecast::autoplot(tsData_gastos, main="Descomposición de los Gastos Diarios")+
theme_bw()+ labs(x="Weeks")
tsdata_gastos_trend<-cbind(tsData,trend=as.vector(tsData_gastos$trend))%>% na.omit()#tsData_gastos$trend
#Using the inputted variables, a Type-2 Sum Squares ANCOVA Lagged Dependent Variable model is fitted which estimates the difference in means between interrupted and non-interrupted time periods, while accounting for the lag of the dependent variable and any further specified covariates.
#Typically such analyses use Auto-regressive Integrated Moving Average (ARIMA) models to handle the serial dependence of the residuals of a linear model, which is estimated either as part of the ARIMA process or through a standard linear regression modeling process [9,17]. All such time series methods enable the effect of the event to be separated from general trends and serial dependencies in time, thereby enabling valid statistical inferences to be made about whether an intervention has had an effect on a time series.
#it uses Type-2 Sum Squares ANCOVA Lagged Dependent Variable model
#ITSA model da cuenta de observaciones autocorrelacionadas e impactos dinámicos mediante una regresión de deltas en rezagados. Una vez que se incorporan en el modelo, se controlan.
#residual autocorrelation assumptions
#TSA allows the model to account for baseline levels and trends present in the data therefore allowing us to attribute significant changes to the interruption
#RDestimate(all~agecell,data=metro_region,cutpoint = 21)
tsdata_gastos_trend<-cbind(tsData,trend=as.vector(tsData_gastos$trend))%>% na.omit()
itsa_metro_region_quar2<-
its.analysis::itsa.model(time = "day", depvar = "trend",data=tsdata_gastos_trend,
interrupt_var = "covid",
alpha = 0.05,no.plots = F, bootstrap = TRUE, Reps = 10000, print = F) 
print(itsa_metro_region_quar2)## [[1]]
## [1] "ITSA Model Fit"
##
## $aov.result
## Anova Table (Type II tests)
##
## Response: depvar
## Sum Sq Df F value Pr(>F)
## interrupt_var 7.7432e+08 2 7.8364 4e-04 ***
## lag_depvar 8.5326e+10 1 1727.0561 <2e-16 ***
## Residuals 3.0681e+10 621
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## $tukey.result
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: stats::aov(formula = x$depvar ~ x$interrupt_var)
##
## $`x$interrupt_var`
## diff lwr upr p adj
## 1-0 7228.838 1131.91 13325.77 0.0151933
## 2-0 28818.026 23269.23 34366.82 0.0000000
## 2-1 21589.188 18306.94 24871.43 0.0000000
##
##
## $data
## depvar interrupt_var lag_depvar
## 2 19269.29 0 16010.00
## 3 24139.00 0 19269.29
## 4 23816.14 0 24139.00
## 5 26510.14 0 23816.14
## 6 23456.71 0 26510.14
## 7 24276.71 0 23456.71
## 8 18818.71 0 24276.71
## 9 18517.14 0 18818.71
## 10 15475.29 0 18517.14
## 11 16365.29 0 15475.29
## 12 12621.29 0 16365.29
## 13 12679.86 0 12621.29
## 14 13440.71 0 12679.86
## 15 15382.86 0 13440.71
## 16 13459.71 0 15382.86
## 17 14644.14 0 13459.71
## 18 13927.00 0 14644.14
## 19 22034.57 0 13927.00
## 20 20986.00 0 22034.57
## 21 20390.57 0 20986.00
## 22 22554.14 0 20390.57
## 23 21782.57 0 22554.14
## 24 22529.57 0 21782.57
## 25 24642.71 0 22529.57
## 26 17692.29 0 24642.71
## 27 19668.29 0 17692.29
## 28 28640.00 0 19668.29
## 29 28706.00 0 28640.00
## 30 28331.57 0 28706.00
## 31 25617.86 0 28331.57
## 32 27223.29 0 25617.86
## 33 31622.57 0 27223.29
## 34 32021.43 0 31622.57
## 35 33634.57 0 32021.43
## 36 30784.86 0 33634.57
## 37 34770.57 0 30784.86
## 38 38443.00 1 34770.57
## 39 35073.00 1 38443.00
## 40 31422.29 1 35073.00
## 41 30103.29 1 31422.29
## 42 19319.29 1 30103.29
## 43 27926.29 1 19319.29
## 44 30715.43 1 27926.29
## 45 31962.29 1 30715.43
## 46 39790.14 1 31962.29
## 47 39211.57 1 39790.14
## 48 44548.57 1 39211.57
## 49 49398.00 1 44548.57
## 50 41039.00 1 49398.00
## 51 34821.29 1 41039.00
## 52 29123.57 1 34821.29
## 53 21275.71 1 29123.57
## 54 28476.14 1 21275.71
## 55 24561.86 1 28476.14
## 56 20323.57 1 24561.86
## 57 25370.00 1 20323.57
## 58 26811.86 1 25370.00
## 59 27151.86 1 26811.86
## 60 27623.29 1 27151.86
## 61 22896.57 1 27623.29
## 62 41889.29 1 22896.57
## 63 44000.14 1 41889.29
## 64 38558.00 1 44000.14
## 65 43373.86 1 38558.00
## 66 49001.00 1 43373.86
## 67 61213.29 1 49001.00
## 68 58939.57 1 61213.29
## 69 42046.86 1 58939.57
## 70 39191.71 1 42046.86
## 71 42646.43 1 39191.71
## 72 36121.57 1 42646.43
## 73 30915.57 1 36121.57
## 74 20273.43 1 30915.57
## 75 23938.29 1 20273.43
## 76 19274.29 1 23938.29
## 77 21662.29 1 19274.29
## 78 15819.00 1 21662.29
## 79 18126.14 1 15819.00
## 80 17240.71 1 18126.14
## 81 16127.71 1 17240.71
## 82 13917.14 1 16127.71
## 83 15379.86 1 13917.14
## 84 19510.14 1 15379.86
## 85 24567.29 1 19510.14
## 86 25700.43 1 24567.29
## 87 25729.00 1 25700.43
## 88 26435.00 1 25729.00
## 89 31157.14 1 26435.00
## 90 29818.43 1 31157.14
## 91 30962.43 1 29818.43
## 92 28746.71 1 30962.43
## 93 27830.71 1 28746.71
## 94 28252.14 1 27830.71
## 95 28717.57 1 28252.14
## 96 21365.43 1 28717.57
## 97 24816.86 1 21365.43
## 98 16838.57 1 24816.86
## 99 15529.14 1 16838.57
## 100 13286.29 1 15529.14
## 101 13629.43 1 13286.29
## 102 14404.86 1 13629.43
## 103 19524.86 1 14404.86
## 104 18475.71 1 19524.86
## 105 22495.00 1 18475.71
## 106 22254.57 1 22495.00
## 107 24173.29 1 22254.57
## 108 27466.43 1 24173.29
## 109 24602.43 1 27466.43
## 110 20531.14 1 24602.43
## 111 20846.43 1 20531.14
## 112 23875.71 1 20846.43
## 113 36312.71 1 23875.71
## 114 34244.00 1 36312.71
## 115 36347.43 1 34244.00
## 116 39779.71 1 36347.43
## 117 42018.71 1 39779.71
## 118 39372.57 1 42018.71
## 119 33444.00 1 39372.57
## 120 29255.86 1 33444.00
## 121 31640.14 1 29255.86
## 122 29671.14 1 31640.14
## 123 31023.71 1 29671.14
## 124 39723.43 1 31023.71
## 125 39314.14 1 39723.43
## 126 38239.86 1 39314.14
## 127 34649.43 1 38239.86
## 128 36688.43 1 34649.43
## 129 42867.57 1 36688.43
## 130 42226.86 1 42867.57
## 131 32155.14 1 42226.86
## 132 33603.00 1 32155.14
## 133 37254.43 1 33603.00
## 134 33145.57 1 37254.43
## 135 31299.43 1 33145.57
## 136 30252.00 1 31299.43
## 137 26310.71 1 30252.00
## 138 27929.86 1 26310.71
## 139 27666.14 1 27929.86
## 140 25017.57 1 27666.14
## 141 27335.00 1 25017.57
## 142 25760.71 1 27335.00
## 143 18436.86 1 25760.71
## 144 21906.00 1 18436.86
## 145 19418.14 1 21906.00
## 146 22826.14 1 19418.14
## 147 23444.29 1 22826.14
## 148 25264.86 1 23444.29
## 149 25473.29 1 25264.86
## 150 27366.86 1 25473.29
## 151 28855.86 1 27366.86
## 152 32326.86 1 28855.86
## 153 27141.43 1 32326.86
## 154 26297.71 1 27141.43
## 155 23499.14 1 26297.71
## 156 30246.29 1 23499.14
## 157 39931.86 1 30246.29
## 158 38020.43 2 39931.86
## 159 35004.00 2 38020.43
## 160 40750.86 2 35004.00
## 161 42363.29 2 40750.86
## 162 46273.57 2 42363.29
## 163 41083.29 2 46273.57
## 164 35711.29 2 41083.29
## 165 41921.71 2 35711.29
## 166 60583.29 2 41921.71
## 167 63115.57 2 60583.29
## 168 61300.14 2 63115.57
## 169 57666.43 2 61300.14
## 170 55834.00 2 57666.43
## 171 58927.71 2 55834.00
## 172 57810.57 2 58927.71
## 173 48987.14 2 57810.57
## 174 52219.29 2 48987.14
## 175 56503.57 2 52219.29
## 176 56545.00 2 56503.57
## 177 64705.57 2 56545.00
## 178 53833.29 2 64705.57
## 179 50114.00 2 53833.29
## 180 39592.43 2 50114.00
## 181 29907.29 2 39592.43
## 182 33923.29 2 29907.29
## 183 45489.00 2 33923.29
## 184 44866.29 2 45489.00
## 185 51680.57 2 44866.29
## 186 58257.00 2 51680.57
## 187 70600.57 2 58257.00
## 188 76648.00 2 70600.57
## 189 69430.14 2 76648.00
## 190 69651.57 2 69430.14
## 191 77745.14 2 69651.57
## 192 72795.86 2 77745.14
## 193 67670.71 2 72795.86
## 194 55357.86 2 67670.71
## 195 48524.00 2 55357.86
## 196 50154.43 2 48524.00
## 197 45111.57 2 50154.43
## 198 36147.00 2 45111.57
## 199 43501.57 2 36147.00
## 200 41472.43 2 43501.57
## 201 41058.00 2 41472.43
## 202 41605.57 2 41058.00
## 203 49382.86 2 41605.57
## 204 59558.57 2 49382.86
## 205 59134.57 2 59558.57
## 206 61109.00 2 59134.57
## 207 63004.43 2 61109.00
## 208 67344.29 2 63004.43
## 209 78180.86 2 67344.29
## 210 69117.86 2 78180.86
## 211 55597.57 2 69117.86
## 212 49426.14 2 55597.57
## 213 39119.43 2 49426.14
## 214 35636.86 2 39119.43
## 215 39201.14 2 35636.86
## 216 27777.00 2 39201.14
## 217 47207.00 2 27777.00
## 218 55587.29 2 47207.00
## 219 56619.71 2 55587.29
## 220 82679.86 2 56619.71
## 221 91259.57 2 82679.86
## 222 93552.71 2 91259.57
## 223 102242.71 2 93552.71
## 224 91884.00 2 102242.71
## 225 85013.86 2 91884.00
## 226 84535.29 2 85013.86
## 227 80700.43 2 84535.29
## 228 79740.57 2 80700.43
## 229 85163.14 2 79740.57
## 230 86724.86 2 85163.14
## 231 80355.00 2 86724.86
## 232 74875.14 2 80355.00
## 233 81347.00 2 74875.14
## 234 66062.43 2 81347.00
## 235 56946.43 2 66062.43
## 236 47732.14 2 56946.43
## 237 38129.71 2 47732.14
## 238 42928.29 2 38129.71
## 239 45392.57 2 42928.29
## 240 37895.43 2 45392.57
## 241 30660.29 2 37895.43
## 242 42430.86 2 30660.29
## 243 35845.14 2 42430.86
## 244 40350.43 2 35845.14
## 245 31494.71 2 40350.43
## 246 30013.29 2 31494.71
## 247 34197.57 2 30013.29
## 248 37430.14 2 34197.57
## 249 26932.43 2 37430.14
## 250 33729.86 2 26932.43
## 251 38081.43 2 33729.86
## 252 44028.00 2 38081.43
## 253 47139.71 2 44028.00
## 254 46558.86 2 47139.71
## 255 58350.57 2 46558.86
## 256 78380.00 2 58350.57
## 257 78168.29 2 78380.00
## 258 70510.86 2 78168.29
## 259 72207.14 2 70510.86
## 260 67881.00 2 72207.14
## 261 69536.43 2 67881.00
## 262 62390.71 2 69536.43
## 263 50113.14 2 62390.71
## 264 45565.57 2 50113.14
## 265 45805.29 2 45565.57
## 266 41348.57 2 45805.29
## 267 51426.86 2 41348.57
## 268 47160.57 2 51426.86
## 269 51907.43 2 47160.57
## 270 49751.43 2 51907.43
## 271 54407.43 2 49751.43
## 272 54746.29 2 54407.43
## 273 61634.57 2 54746.29
## 274 58926.43 2 61634.57
## 275 69999.29 2 58926.43
## 276 63044.86 2 69999.29
## 277 63285.29 2 63044.86
## 278 61395.43 2 63285.29
## 279 67969.43 2 61395.43
## 280 60792.57 2 67969.43
## 281 56859.14 2 60792.57
## 282 44899.43 2 56859.14
## 283 43064.14 2 44899.43
## 284 62790.29 2 43064.14
## 285 69120.71 2 62790.29
## 286 69589.43 2 69120.71
## 287 66633.29 2 69589.43
## 288 65588.57 2 66633.29
## 289 70168.57 2 65588.57
## 290 74644.71 2 70168.57
## 291 52891.00 2 74644.71
## 292 41560.57 2 52891.00
## 293 34704.86 2 41560.57
## 294 46520.00 2 34704.86
## 295 50231.00 2 46520.00
## 296 49216.71 2 50231.00
## 297 76914.86 2 49216.71
## 298 83720.71 2 76914.86
## 299 84485.00 2 83720.71
## 300 89765.00 2 84485.00
## 301 87702.86 2 89765.00
## 302 82013.86 2 87702.86
## 303 85982.43 2 82013.86
## 304 57248.43 2 85982.43
## 305 52968.43 2 57248.43
## 306 52601.86 2 52968.43
## 307 45493.29 2 52601.86
## 308 42298.86 2 45493.29
## 309 46423.71 2 42298.86
## 310 37898.00 2 46423.71
## 311 36435.14 2 37898.00
## 312 30209.57 2 36435.14
## 313 34541.86 2 30209.57
## 314 33604.71 2 34541.86
## 315 37990.71 2 33604.71
## 316 35683.43 2 37990.71
## 317 65201.86 2 35683.43
## 318 62730.57 2 65201.86
## 319 64589.14 2 62730.57
## 320 73744.86 2 64589.14
## 321 76477.71 2 73744.86
## 322 105647.43 2 76477.71
## 323 103790.29 2 105647.43
## 324 76122.29 2 103790.29
## 325 74746.14 2 76122.29
## 326 72865.71 2 74746.14
## 327 63652.57 2 72865.71
## 328 60358.29 2 63652.57
## 329 25957.14 2 60358.29
## 330 30178.43 2 25957.14
## 331 30681.57 2 30178.43
## 332 33337.29 2 30681.57
## 333 32582.71 2 33337.29
## 334 39184.43 2 32582.71
## 335 40415.71 2 39184.43
## 336 34975.43 2 40415.71
## 337 34076.14 2 34975.43
## 338 34221.14 2 34076.14
## 339 28862.57 2 34221.14
## 340 35729.86 2 28862.57
## 341 36489.29 2 35729.86
## 342 36785.14 2 36489.29
## 343 37787.71 2 36785.14
## 344 39832.14 2 37787.71
## 345 41917.86 2 39832.14
## 346 41633.57 2 41917.86
## 347 33557.00 2 41633.57
## 348 22759.57 2 33557.00
## 349 28877.86 2 22759.57
## 350 27574.00 2 28877.86
## 351 27104.71 2 27574.00
## 352 24376.14 2 27104.71
## 353 29732.29 2 24376.14
## 354 34030.00 2 29732.29
## 355 39139.71 2 34030.00
## 356 37066.57 2 39139.71
## 357 38509.29 2 37066.57
## 358 40957.29 2 38509.29
## 359 49423.00 2 40957.29
## 360 50053.29 2 49423.00
## 361 50284.14 2 50053.29
## 362 53103.86 2 50284.14
## 363 50223.00 2 53103.86
## 364 49587.14 2 50223.00
## 365 41167.71 2 49587.14
## 366 37958.71 2 41167.71
## 367 33582.29 2 37958.71
## 368 31039.43 2 33582.29
## 369 26526.57 2 31039.43
## 370 34869.43 2 26526.57
## 371 37487.43 2 34869.43
## 372 46514.43 2 37487.43
## 373 39613.43 2 46514.43
## 374 38980.57 2 39613.43
## 375 37306.14 2 38980.57
## 376 36771.29 2 37306.14
## 377 26317.00 2 36771.29
## 378 31580.71 2 26317.00
## 379 23626.57 2 31580.71
## 380 33035.71 2 23626.57
## 381 44864.57 2 33035.71
## 382 48946.14 2 44864.57
## 383 46969.57 2 48946.14
## 384 49249.57 2 46969.57
## 385 56370.14 2 49249.57
## 386 67228.71 2 56370.14
## 387 59457.29 2 67228.71
## 388 53124.71 2 59457.29
## 389 52814.14 2 53124.71
## 390 61262.00 2 52814.14
## 391 61861.14 2 61262.00
## 392 71784.71 2 61861.14
## 393 59313.29 2 71784.71
## 394 61107.00 2 59313.29
## 395 60603.43 2 61107.00
## 396 60012.57 2 60603.43
## 397 58280.43 2 60012.57
## 398 56862.71 2 58280.43
## 399 41704.43 2 56862.71
## 400 51533.00 2 41704.43
## 401 50388.71 2 51533.00
## 402 49205.29 2 50388.71
## 403 56533.29 2 49205.29
## 404 47996.14 2 56533.29
## 405 47207.57 2 47996.14
## 406 45292.00 2 47207.57
## 407 40343.43 2 45292.00
## 408 39004.86 2 40343.43
## 409 36788.43 2 39004.86
## 410 30027.57 2 36788.43
## 411 39040.14 2 30027.57
## 412 42390.14 2 39040.14
## 413 36291.14 2 42390.14
## 414 30668.29 2 36291.14
## 415 47693.00 2 30668.29
## 416 52094.43 2 47693.00
## 417 56592.57 2 52094.43
## 418 47971.43 2 56592.57
## 419 43762.43 2 47971.43
## 420 42246.71 2 43762.43
## 421 46352.43 2 42246.71
## 422 33094.86 2 46352.43
## 423 32784.86 2 33094.86
## 424 26212.43 2 32784.86
## 425 32611.57 2 26212.43
## 426 42144.86 2 32611.57
## 427 50034.86 2 42144.86
## 428 46332.00 2 50034.86
## 429 42976.29 2 46332.00
## 430 39456.29 2 42976.29
## 431 39328.29 2 39456.29
## 432 35296.14 2 39328.29
## 433 30875.43 2 35296.14
## 434 27709.00 2 30875.43
## 435 29513.29 2 27709.00
## 436 31630.43 2 29513.29
## 437 29346.14 2 31630.43
## 438 34916.86 2 29346.14
## 439 42020.86 2 34916.86
## 440 38303.00 2 42020.86
## 441 37966.43 2 38303.00
## 442 41408.14 2 37966.43
## 443 38988.14 2 41408.14
## 444 43555.29 2 38988.14
## 445 38114.00 2 43555.29
## 446 27847.86 2 38114.00
## 447 26517.00 2 27847.86
## 448 39518.29 2 26517.00
## 449 39153.71 2 39518.29
## 450 45623.14 2 39153.71
## 451 40627.43 2 45623.14
## 452 41027.71 2 40627.43
## 453 42882.86 2 41027.71
## 454 47139.43 2 42882.86
## 455 35547.57 2 47139.43
## 456 41099.00 2 35547.57
## 457 35859.57 2 41099.00
## 458 44524.57 2 35859.57
## 459 48554.29 2 44524.57
## 460 51554.29 2 48554.29
## 461 47810.29 2 51554.29
## 462 50490.00 2 47810.29
## 463 50720.71 2 50490.00
## 464 52720.71 2 50720.71
## 465 52145.57 2 52720.71
## 466 55515.57 2 52145.57
## 467 52457.00 2 55515.57
## 468 58239.57 2 52457.00
## 469 50523.57 2 58239.57
## 470 47788.57 2 50523.57
## 471 46170.00 2 47788.57
## 472 42305.57 2 46170.00
## 473 46605.57 2 42305.57
## 474 55149.57 2 46605.57
## 475 48769.57 2 55149.57
## 476 50719.43 2 48769.57
## 477 44753.71 2 50719.43
## 478 42898.00 2 44753.71
## 479 46141.14 2 42898.00
## 480 34022.57 2 46141.14
## 481 26651.86 2 34022.57
## 482 28791.86 2 26651.86
## 483 31879.00 2 28791.86
## 484 33584.71 2 31879.00
## 485 34690.43 2 33584.71
## 486 27410.43 2 34690.43
## 487 41755.00 2 27410.43
## 488 49379.57 2 41755.00
## 489 57198.86 2 49379.57
## 490 51144.57 2 57198.86
## 491 56677.43 2 51144.57
## 492 65416.43 2 56677.43
## 493 69779.71 2 65416.43
## 494 54046.00 2 69779.71
## 495 43259.57 2 54046.00
## 496 40998.57 2 43259.57
## 497 41368.57 2 40998.57
## 498 42274.29 2 41368.57
## 499 35962.71 2 42274.29
## 500 38709.00 2 35962.71
## 501 44778.14 2 38709.00
## 502 51282.43 2 44778.14
## 503 52094.86 2 51282.43
## 504 52221.43 2 52094.86
## 505 45011.43 2 52221.43
## 506 46545.43 2 45011.43
## 507 42263.00 2 46545.43
## 508 45417.43 2 42263.00
## 509 45034.71 2 45417.43
## 510 37840.57 2 45034.71
## 511 39135.43 2 37840.57
## 512 38191.14 2 39135.43
## 513 39456.86 2 38191.14
## 514 42479.14 2 39456.86
## 515 34282.57 2 42479.14
## 516 28878.43 2 34282.57
## 517 56227.14 2 28878.43
## 518 65569.43 2 56227.14
## 519 69751.29 2 65569.43
## 520 62171.71 2 69751.29
## 521 63705.14 2 62171.71
## 522 79257.86 2 63705.14
## 523 87244.71 2 79257.86
## 524 58568.00 2 87244.71
## 525 52695.29 2 58568.00
## 526 48911.00 2 52695.29
## 527 53924.00 2 48911.00
## 528 53358.86 2 53924.00
## 529 42121.14 2 53358.86
## 530 47835.71 2 42121.14
## 531 62329.29 2 47835.71
## 532 56056.86 2 62329.29
## 533 59946.43 2 56056.86
## 534 64511.57 2 59946.43
## 535 61137.43 2 64511.57
## 536 55448.71 2 61137.43
## 537 47964.43 2 55448.71
## 538 46425.71 2 47964.43
## 539 55512.00 2 46425.71
## 540 55226.29 2 55512.00
## 541 46709.14 2 55226.29
## 542 49254.71 2 46709.14
## 543 49056.29 2 49254.71
## 544 49850.57 2 49056.29
## 545 39145.71 2 49850.57
## 546 29799.43 2 39145.71
## 547 34769.86 2 29799.43
## 548 44061.57 2 34769.86
## 549 43829.14 2 44061.57
## 550 45782.00 2 43829.14
## 551 38924.57 2 45782.00
## 552 49242.43 2 38924.57
## 553 50565.00 2 49242.43
## 554 38864.43 2 50565.00
## 555 49786.71 2 38864.43
## 556 58787.86 2 49786.71
## 557 58060.86 2 58787.86
## 558 62179.43 2 58060.86
## 559 57333.86 2 62179.43
## 560 70797.00 2 57333.86
## 561 89901.71 2 70797.00
## 562 78558.14 2 89901.71
## 563 65466.00 2 78558.14
## 564 70525.00 2 65466.00
## 565 68377.86 2 70525.00
## 566 69736.29 2 68377.86
## 567 60085.86 2 69736.29
## 568 41757.00 2 60085.86
## 569 49780.29 2 41757.00
## 570 56540.29 2 49780.29
## 571 57894.29 2 56540.29
## 572 60270.29 2 57894.29
## 573 61011.00 2 60270.29
## 574 57721.43 2 61011.00
## 575 71741.00 2 57721.43
## 576 59576.00 2 71741.00
## 577 52390.29 2 59576.00
## 578 61092.29 2 52390.29
## 579 62814.00 2 61092.29
## 580 54908.29 2 62814.00
## 581 62082.00 2 54908.29
## 582 57017.71 2 62082.00
## 583 53634.43 2 57017.71
## 584 69169.00 2 53634.43
## 585 52488.14 2 69169.00
## 586 60895.57 2 52488.14
## 587 59856.57 2 60895.57
## 588 52670.00 2 59856.57
## 589 51874.57 2 52670.00
## 590 52190.57 2 51874.57
## 591 41562.43 2 52190.57
## 592 44764.14 2 41562.43
## 593 38612.71 2 44764.14
## 594 43473.14 2 38612.71
## 595 53505.00 2 43473.14
## 596 45870.86 2 53505.00
## 597 52578.00 2 45870.86
## 598 55300.00 2 52578.00
## 599 61789.71 2 55300.00
## 600 57391.71 2 61789.71
## 601 62902.29 2 57391.71
## 602 53250.43 2 62902.29
## 603 55402.57 2 53250.43
## 604 56291.29 2 55402.57
## 605 58933.57 2 56291.29
## 606 59590.71 2 58933.57
## 607 59065.00 2 59590.71
## 608 52399.57 2 59065.00
## 609 60483.43 2 52399.57
## 610 58262.71 2 60483.43
## 611 54939.71 2 58262.71
## 612 51169.00 2 54939.71
## 613 43113.29 2 51169.00
## 614 56289.71 2 43113.29
## 615 60739.86 2 56289.71
## 616 50363.14 2 60739.86
## 617 62270.86 2 50363.14
## 618 67061.57 2 62270.86
## 619 59609.00 2 67061.57
## 620 85054.00 2 59609.00
## 621 68023.29 2 85054.00
## 622 59242.29 2 68023.29
## 623 61535.14 2 59242.29
## 624 56215.86 2 61535.14
## 625 45152.29 2 56215.86
## 626 57409.57 2 45152.29
##
## $alpha
## [1] 0.05
##
## $itsa.result
## [1] "Significant variation between time periods with chosen alpha"
##
## $group.means
## interrupt_var count mean s.d.
## 1 0 37 22066.04 6308.636
## 2 1 120 29463.10 9187.258
## 3 2 469 51052.29 14947.707
##
## $dependent
## [1] 19269.29 24139.00 23816.14 26510.14 23456.71 24276.71 18818.71
## [8] 18517.14 15475.29 16365.29 12621.29 12679.86 13440.71 15382.86
## [15] 13459.71 14644.14 13927.00 22034.57 20986.00 20390.57 22554.14
## [22] 21782.57 22529.57 24642.71 17692.29 19668.29 28640.00 28706.00
## [29] 28331.57 25617.86 27223.29 31622.57 32021.43 33634.57 30784.86
## [36] 34770.57 38443.00 35073.00 31422.29 30103.29 19319.29 27926.29
## [43] 30715.43 31962.29 39790.14 39211.57 44548.57 49398.00 41039.00
## [50] 34821.29 29123.57 21275.71 28476.14 24561.86 20323.57 25370.00
## [57] 26811.86 27151.86 27623.29 22896.57 41889.29 44000.14 38558.00
## [64] 43373.86 49001.00 61213.29 58939.57 42046.86 39191.71 42646.43
## [71] 36121.57 30915.57 20273.43 23938.29 19274.29 21662.29 15819.00
## [78] 18126.14 17240.71 16127.71 13917.14 15379.86 19510.14 24567.29
## [85] 25700.43 25729.00 26435.00 31157.14 29818.43 30962.43 28746.71
## [92] 27830.71 28252.14 28717.57 21365.43 24816.86 16838.57 15529.14
## [99] 13286.29 13629.43 14404.86 19524.86 18475.71 22495.00 22254.57
## [106] 24173.29 27466.43 24602.43 20531.14 20846.43 23875.71 36312.71
## [113] 34244.00 36347.43 39779.71 42018.71 39372.57 33444.00 29255.86
## [120] 31640.14 29671.14 31023.71 39723.43 39314.14 38239.86 34649.43
## [127] 36688.43 42867.57 42226.86 32155.14 33603.00 37254.43 33145.57
## [134] 31299.43 30252.00 26310.71 27929.86 27666.14 25017.57 27335.00
## [141] 25760.71 18436.86 21906.00 19418.14 22826.14 23444.29 25264.86
## [148] 25473.29 27366.86 28855.86 32326.86 27141.43 26297.71 23499.14
## [155] 30246.29 39931.86 38020.43 35004.00 40750.86 42363.29 46273.57
## [162] 41083.29 35711.29 41921.71 60583.29 63115.57 61300.14 57666.43
## [169] 55834.00 58927.71 57810.57 48987.14 52219.29 56503.57 56545.00
## [176] 64705.57 53833.29 50114.00 39592.43 29907.29 33923.29 45489.00
## [183] 44866.29 51680.57 58257.00 70600.57 76648.00 69430.14 69651.57
## [190] 77745.14 72795.86 67670.71 55357.86 48524.00 50154.43 45111.57
## [197] 36147.00 43501.57 41472.43 41058.00 41605.57 49382.86 59558.57
## [204] 59134.57 61109.00 63004.43 67344.29 78180.86 69117.86 55597.57
## [211] 49426.14 39119.43 35636.86 39201.14 27777.00 47207.00 55587.29
## [218] 56619.71 82679.86 91259.57 93552.71 102242.71 91884.00 85013.86
## [225] 84535.29 80700.43 79740.57 85163.14 86724.86 80355.00 74875.14
## [232] 81347.00 66062.43 56946.43 47732.14 38129.71 42928.29 45392.57
## [239] 37895.43 30660.29 42430.86 35845.14 40350.43 31494.71 30013.29
## [246] 34197.57 37430.14 26932.43 33729.86 38081.43 44028.00 47139.71
## [253] 46558.86 58350.57 78380.00 78168.29 70510.86 72207.14 67881.00
## [260] 69536.43 62390.71 50113.14 45565.57 45805.29 41348.57 51426.86
## [267] 47160.57 51907.43 49751.43 54407.43 54746.29 61634.57 58926.43
## [274] 69999.29 63044.86 63285.29 61395.43 67969.43 60792.57 56859.14
## [281] 44899.43 43064.14 62790.29 69120.71 69589.43 66633.29 65588.57
## [288] 70168.57 74644.71 52891.00 41560.57 34704.86 46520.00 50231.00
## [295] 49216.71 76914.86 83720.71 84485.00 89765.00 87702.86 82013.86
## [302] 85982.43 57248.43 52968.43 52601.86 45493.29 42298.86 46423.71
## [309] 37898.00 36435.14 30209.57 34541.86 33604.71 37990.71 35683.43
## [316] 65201.86 62730.57 64589.14 73744.86 76477.71 105647.43 103790.29
## [323] 76122.29 74746.14 72865.71 63652.57 60358.29 25957.14 30178.43
## [330] 30681.57 33337.29 32582.71 39184.43 40415.71 34975.43 34076.14
## [337] 34221.14 28862.57 35729.86 36489.29 36785.14 37787.71 39832.14
## [344] 41917.86 41633.57 33557.00 22759.57 28877.86 27574.00 27104.71
## [351] 24376.14 29732.29 34030.00 39139.71 37066.57 38509.29 40957.29
## [358] 49423.00 50053.29 50284.14 53103.86 50223.00 49587.14 41167.71
## [365] 37958.71 33582.29 31039.43 26526.57 34869.43 37487.43 46514.43
## [372] 39613.43 38980.57 37306.14 36771.29 26317.00 31580.71 23626.57
## [379] 33035.71 44864.57 48946.14 46969.57 49249.57 56370.14 67228.71
## [386] 59457.29 53124.71 52814.14 61262.00 61861.14 71784.71 59313.29
## [393] 61107.00 60603.43 60012.57 58280.43 56862.71 41704.43 51533.00
## [400] 50388.71 49205.29 56533.29 47996.14 47207.57 45292.00 40343.43
## [407] 39004.86 36788.43 30027.57 39040.14 42390.14 36291.14 30668.29
## [414] 47693.00 52094.43 56592.57 47971.43 43762.43 42246.71 46352.43
## [421] 33094.86 32784.86 26212.43 32611.57 42144.86 50034.86 46332.00
## [428] 42976.29 39456.29 39328.29 35296.14 30875.43 27709.00 29513.29
## [435] 31630.43 29346.14 34916.86 42020.86 38303.00 37966.43 41408.14
## [442] 38988.14 43555.29 38114.00 27847.86 26517.00 39518.29 39153.71
## [449] 45623.14 40627.43 41027.71 42882.86 47139.43 35547.57 41099.00
## [456] 35859.57 44524.57 48554.29 51554.29 47810.29 50490.00 50720.71
## [463] 52720.71 52145.57 55515.57 52457.00 58239.57 50523.57 47788.57
## [470] 46170.00 42305.57 46605.57 55149.57 48769.57 50719.43 44753.71
## [477] 42898.00 46141.14 34022.57 26651.86 28791.86 31879.00 33584.71
## [484] 34690.43 27410.43 41755.00 49379.57 57198.86 51144.57 56677.43
## [491] 65416.43 69779.71 54046.00 43259.57 40998.57 41368.57 42274.29
## [498] 35962.71 38709.00 44778.14 51282.43 52094.86 52221.43 45011.43
## [505] 46545.43 42263.00 45417.43 45034.71 37840.57 39135.43 38191.14
## [512] 39456.86 42479.14 34282.57 28878.43 56227.14 65569.43 69751.29
## [519] 62171.71 63705.14 79257.86 87244.71 58568.00 52695.29 48911.00
## [526] 53924.00 53358.86 42121.14 47835.71 62329.29 56056.86 59946.43
## [533] 64511.57 61137.43 55448.71 47964.43 46425.71 55512.00 55226.29
## [540] 46709.14 49254.71 49056.29 49850.57 39145.71 29799.43 34769.86
## [547] 44061.57 43829.14 45782.00 38924.57 49242.43 50565.00 38864.43
## [554] 49786.71 58787.86 58060.86 62179.43 57333.86 70797.00 89901.71
## [561] 78558.14 65466.00 70525.00 68377.86 69736.29 60085.86 41757.00
## [568] 49780.29 56540.29 57894.29 60270.29 61011.00 57721.43 71741.00
## [575] 59576.00 52390.29 61092.29 62814.00 54908.29 62082.00 57017.71
## [582] 53634.43 69169.00 52488.14 60895.57 59856.57 52670.00 51874.57
## [589] 52190.57 41562.43 44764.14 38612.71 43473.14 53505.00 45870.86
## [596] 52578.00 55300.00 61789.71 57391.71 62902.29 53250.43 55402.57
## [603] 56291.29 58933.57 59590.71 59065.00 52399.57 60483.43 58262.71
## [610] 54939.71 51169.00 43113.29 56289.71 60739.86 50363.14 62270.86
## [617] 67061.57 59609.00 85054.00 68023.29 59242.29 61535.14 56215.86
## [624] 45152.29 57409.57
##
## $interrupt_var
## [1] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1
## [38] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [75] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [112] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [149] 1 1 1 1 1 1 1 1 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [186] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [223] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [260] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [297] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [334] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [371] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [408] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [445] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [482] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [519] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [556] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [593] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## Levels: 0 1 2
##
## $residuals
## 2 3 4 5 6 7
## 1928.87713 4001.80220 -499.75100 2471.29233 -2893.85445 546.28899
## 8 9 10 11 12 13
## -5615.35203 -1233.42013 -4016.49919 -516.28518 -5023.99312 -1752.69973
## 14 15 16 17 18 19
## -1042.10266 247.14958 -3342.54376 -507.86858 -2241.36819 6481.58267
## 20 21 22 23 24 25
## -1524.08619 -1219.73686 1454.77112 -1173.35839 235.72614 1707.86918
## 26 27 28 29 30 31
## -7055.84477 884.30975 8160.42078 527.80338 96.74029 -2295.67729
## 32 33 34 35 36 37
## 1638.38630 4660.05566 1283.89094 2554.77496 -1679.17530 4751.87543
## 38 39 40 41 42 43
## 4388.65761 -2132.64899 -2891.57027 -1077.89667 -10730.06433 7130.67329
## 44 45 46 47 48 49
## 2534.15965 1387.65661 8145.58711 849.94075 6683.41220 6953.16740
## 50 51 52 53 54 55
## -5567.12151 -4611.98780 -4974.28883 -7932.94409 6001.72139 -4091.24355
## 56 57 58 59 60 61
## -4970.68533 3712.61119 824.13396 -73.12195 106.55303 -5024.69356
## 62 63 64 65 66 67
## 18024.00866 3837.24018 -3416.22669 6069.52679 7564.18834 14947.82943
## 68 69 70 71 72 73
## 2194.76725 -12746.87551 -1106.40025 4798.30874 -4691.03464 -4298.06246
## 74 75 76 77 78 79
## -10472.94288 2323.92444 -5484.88514 905.28774 -6987.13795 334.12146
## 80 81 82 83 84 85
## -2531.06367 -2884.27846 -4139.78591 -780.18290 2094.94942 3607.89900
## 86 87 88 89 90 91
## 401.51357 -542.26355 139.21934 4255.54444 -1135.23504 1157.51410
## 92 93 94 95 96 97
## -2039.86523 -1054.56342 152.88366 256.68488 -7494.84169 2265.45196
## 98 99 100 101 102 103
## -8674.50052 -3137.77158 -4257.00961 -1989.27370 -1508.29561 2946.31005
## 104 105 106 107 108 109
## -2496.29874 2423.25522 -1266.11766 858.90810 2505.60450 -3184.23748
## 110 111 112 113 114 115
## -4797.92819 -989.07703 1769.66239 11607.23590 -1133.65335 2744.93650
## 116 117 118 119 120 121
## 4372.27265 3666.03235 -901.39376 -4559.31313 -3660.15587 2317.97026
## 122 123 124 125 126 127
## -1696.98248 1345.18551 8884.25986 1009.75962 286.68150 -2381.90378
## 128 129 130 131 132 133
## 2738.03873 7167.51810 1224.48865 -8297.42947 1792.95377 4201.97784
## 134 135 136 137 138 139
## -3040.16583 -1360.50324 -823.75881 -3866.24731 1134.90815 -518.19071
## 140 141 142 143 144 145
## -2940.46922 1649.69536 -1913.17307 -7886.13751 1867.59849 -3597.12603
## 146 147 148 149 150 151
## 1945.70124 -360.55667 929.58711 -424.21451 1290.50461 1154.63321
## 152 153 154 155 156 157
## 3347.92408 -4815.96548 -1210.06966 -3284.65086 5863.94283 9759.79897
## 158 159 160 161 162 163
## -3521.40866 -4897.64264 3437.60829 118.66555 2645.32822 -5900.36903
## 164 165 166 167 168 169
## -6818.59101 4001.54440 17333.95443 3852.76781 -135.61213 -2211.50931
## 170 171 172 173 174 175
## -925.85196 3740.26711 -31.58831 -7896.39792 2907.11854 4417.90630
## 176 177 178 179 180 181
## 782.99438 8908.01600 -8966.84639 -3356.63662 -10686.69338 -11343.28826
## 182 183 184 185 186 187
## 983.52135 9103.11079 -1444.12922 5904.50689 6633.60496 13333.95083
## 188 189 190 191 192 193
## 8789.37538 -3617.77305 2797.29018 10700.85401 -1193.51575 -2071.68240
## 194 195 196 197 198 199
## -9986.66053 -6254.86955 1239.68374 -5202.24223 -9839.54392 5207.51563
## 200 201 202 203 204 205
## -3132.57626 -1805.79975 -902.60765 6404.80767 9906.84230 751.07392
## 206 207 208 209 210 211
## 3089.33639 3290.51015 6003.90228 13116.44744 -5245.40167 -10988.73782
## 212 213 214 215 216 217
## -5558.42524 -10569.44398 -5207.83609 1344.84004 -13137.81217 16095.23128
## 218 219 220 221 222 223
## 7802.65702 1643.97235 26818.18947 13035.72605 7966.62625 14688.88308
## 224 225 226 227 228 229
## -3126.70991 -1108.05231 4308.63770 884.44213 3215.27136 9461.49507
## 230 231 232 233 234 235
## 6370.10724 -1339.85509 -1353.74536 9820.37070 -11017.69367 -7018.02104
## 236 237 238 239 240 241
## -8409.87796 -10105.53887 2932.86513 1279.50239 -8332.24112 -9134.09457
## 242 243 244 245 246 247
## 8844.94437 -7841.08331 2315.39603 -10406.29859 -4288.64923 1166.84859
## 248 249 250 251 252 253
## 808.88940 -12462.69060 3342.81417 1861.52019 4074.01333 2082.98172
## 254 255 256 257 258 259
## -1168.03376 11122.11335 21033.08587 3634.14266 -3841.61414 4425.50196
## 260 261 262 263 264 265
## -1356.22166 4011.46497 -4554.77061 -10700.61308 -4712.81501 -570.83507
## 266 267 268 269 270 271
## -5233.24790 8669.33906 -4245.11168 4162.64018 -2066.63231 4439.42873
## 272 273 274 275 276 277
## 782.97779 7380.49059 -1238.48207 12158.22925 -4297.80492 1910.21057
## 278 279 280 281 282 283
## -185.95804 8009.72613 -4808.27262 -2583.24858 -11167.69247 -2740.36153
## 284 285 286 287 288 289
## 18560.63782 7964.08658 3000.66761 -357.67842 1134.26998 6610.73807
## 290 291 292 293 294 295
## 7156.78835 -18437.89881 -11101.49093 -8234.57789 9463.44528 3035.88519
## 296 297 298 299 300 301
## -1162.80523 27405.69500 10443.80858 5367.99634 9992.16367 3399.25907
## 302 303 304 305 306 307
## -520.21858 8330.07700 -23809.34944 -3432.73808 -126.64658 -6920.66350
## 308 309 310 311 312 313
## -4015.23536 2850.75716 -9214.49215 -3361.44397 -8331.73942 1342.70176
## 314 315 316 317 318 319
## -3311.97033 1878.19084 -4192.71627 27305.59143 -495.42286 3483.75591
## 320 321 322 323 324 325
## 11044.63226 5920.98182 32745.63463 5857.99451 -20216.39341 2149.34175
## 326 327 328 329 330 331
## 1449.77974 -6149.76959 -1538.26834 -33112.58853 628.27711 -2490.86030
## 332 333 334 335 336 337
## -266.89231 -3300.32902 3948.88212 -484.75537 -6981.60590 -3212.58889
## 338 339 340 341 342 343
## -2295.91288 -7778.90864 3686.56087 -1446.82030 -1802.62791 -1053.93115
## 344 345 346 347 348 349
## 130.19207 461.58462 -1612.45005 -9445.07624 -13312.00844 2071.53794
## 350 351 352 353 354 355
## -4482.41293 -3832.86037 -6158.73829 1538.78848 1240.40293 2662.25368
## 356 357 358 359 360 361
## -3795.52896 -573.85324 636.15534 7001.24373 367.11004 57.11976
## 362 363 364 365 366 367
## 2678.73581 -2621.71482 -785.51187 -8659.31218 -4643.61050 -6266.39997
## 368 369 370 371 372 373
## -5053.84894 -7384.68338 4830.65113 289.65530 7070.15260 -7576.90540
## 374 375 376 377 378 379
## -2288.02262 -3419.39723 -2517.42920 -12512.75463 1721.76984 -10749.16001
## 380 381 382 383 384 385
## 5485.42341 9240.30621 3171.54934 -2307.41369 1668.67991 6832.78604
## 386 387 388 389 390 391
## 11581.20356 -5507.95229 -5171.87007 -48.46946 8665.88866 2015.93531
## 392 393 394 395 396 397
## 11425.38297 -9561.45049 2933.98188 891.22635 732.48325 -492.64579
## 398 399 400 401 402 403
## -424.01035 -14365.75711 8470.12133 -1108.04988 -1309.56824 7033.93041
## 404 405 406 407 408 409
## -7791.36056 -1254.21982 -2493.11904 -5797.94089 -2890.14904 -3957.95106
## 410 411 412 413 414 415
## -8816.89347 5997.16146 1613.48460 -7360.14643 -7749.45890 14100.22244
## 416 417 418 419 420 421
## 3892.76385 4614.04607 -7866.94785 -4678.15538 -2582.13175 2824.21517
## 422 423 424 425 426 427
## -13956.46482 -2890.15849 -9196.57643 2842.35960 6884.54841 6594.04723
## 428 429 430 431 432 433
## -3879.20956 -4057.50652 -4697.97207 -1805.46424 -5727.77045 -6688.50772
## 434 435 436 437 438 439
## -6061.52661 -1540.13229 -971.24486 -5072.24835 2458.60880 4782.38547
## 440 441 442 443 444 445
## -5031.40566 -2177.68828 1552.83755 -3820.49340 2823.24859 -6537.09699
## 446 447 448 449 450 451
## -12134.07903 -4655.57115 9487.72150 -2033.23779 4749.02909 -5798.09419
## 452 453 454 455 456 457
## -1110.99196 400.66620 3065.34174 -12179.07430 3319.31315 -6683.78971
## 458 459 460 461 462 463
## 6477.15774 3071.44580 2613.55274 -3704.74370 2187.69255 118.94718
## 464 465 466 467 468 469
## 1920.97152 -370.36896 3493.16044 -2457.20399 5949.92395 -6728.09374
## 470 471 472 473 474 475
## -2842.00328 -2113.67445 -4589.20880 3026.85278 7881.02788 -5830.56840
## 476 477 478 479 480 481
## 1593.95918 -6044.92521 -2781.46713 2054.06196 -12847.44652 -9819.22902
## 482 483 484 485 486 487
## -1354.42782 -103.61643 -1046.97577 -1404.93289 -9633.74501 10957.78579
## 488 489 490 491 492 493
## 6273.29747 7549.94748 -5214.05804 5513.97450 9505.23634 6369.59651
## 494 495 496 497 498 499
## -13108.24808 -10393.59506 -3398.77349 -1088.61207 -500.39434 -7589.15813
## 500 501 502 503 504 505
## 573.07955 4285.63789 5581.99931 813.10802 242.53545 -7076.07534
## 506 507 508 509 510 511
## 644.81712 -4953.93504 1875.24042 -1214.28529 -8080.02147 -611.87887
## 512 513 514 515 516 517
## -2667.27996 -591.27522 1344.90256 -9445.08866 -7815.76328 24170.23959
## 518 519 520 521 522 523
## 10044.62598 6209.87882 -4958.13927 3079.31056 17316.19161 11957.28305
## 524 525 526 527 528 529
## -23572.94388 -4838.20362 -3583.12016 4677.17092 -189.62129 -10942.38716
## 530 531 532 533 534 535
## 4415.25358 14005.15803 -4704.18701 4567.74795 5795.25417 -1496.23236
## 536 537 538 539 540 541
## -4289.59866 -6892.40539 -2008.86297 8397.79165 315.14636 -7956.82541
## 542 543 544 545 546 547
## 1897.29621 -485.48418 479.07285 -10907.35992 -11067.82041 1922.64486
## 548 549 550 551 552 553
## 6949.24028 -1256.39731 895.90652 -7637.26646 8564.94202 1033.77246
## 554 555 556 557 558 559
## -11801.69595 9160.83625 8789.57867 338.70865 5081.11792 -3298.59477
## 560 561 562 563 564 565
## 14322.52719 21874.53454 -5862.76958 -9221.00723 7072.34504 584.08028
## 566 567 568 569 570 571
## 3784.96959 -7031.12493 -17078.96075 6672.29556 6547.52360 2100.77561
## 572 573 574 575 576 577
## 3314.90981 2016.78131 -1908.39617 14933.95261 -9261.22503 -6008.16723
## 578 579 580 581 582 583
## 8859.88569 3114.42409 -6268.69119 7688.90716 -3531.13428 -2568.76243
## 584 585 586 587 588 589
## 15869.00246 -14142.05202 8579.20030 325.79581 -5969.21095 -597.85109
## 590 591 592 593 594 595
## 400.70522 -10498.59686 1823.11422 -7075.70159 3063.26055 8924.38969
## 596 597 598 599 600 601
## -7318.07789 5939.91391 2906.52257 7060.49188 -2906.32426 6378.16576
## 602 603 604 605 606 607
## -8002.30620 2432.08384 1474.04689 3353.72792 1743.52854 653.92075
## 608 609 610 611 612 613
## -5560.39302 8243.06049 -914.40204 -2331.80974 -3251.06166 -8071.13049
## 614 615 616 617 618 619
## 12017.89697 5161.36208 -9034.01452 11777.94600 6350.66477 -5212.81291
## 620 621 622 623 624 625
## 26627.22980 -12237.80884 -6404.77309 3423.04975 -3863.73396 -10362.83262
## 626
## 11388.09063
##
## $fitted.values
## 2 3 4 5 6 7 8 9
## 17340.41 20137.20 24315.89 24038.85 26350.57 23730.43 24434.07 19750.56
## 10 11 12 13 14 15 16 17
## 19491.78 16881.57 17645.28 14432.56 14482.82 15135.71 16802.26 15152.01
## 18 19 20 21 22 23 24 25
## 16168.37 15552.99 22510.09 21610.31 21099.37 22955.93 22293.85 22934.85
## 26 27 28 29 30 31 32 33
## 24748.13 18783.98 20479.58 28178.20 28234.83 27913.53 25584.90 26962.52
## 34 35 36 37 38 39 40 41
## 30737.54 31079.80 32464.03 30018.70 34054.34 37205.65 34313.86 31181.18
## 42 43 44 45 46 47 48 49
## 30049.35 20795.61 28181.27 30574.63 31644.56 38361.63 37865.16 42444.83
## 50 51 52 53 54 55 56 57
## 46606.12 39433.27 34097.86 29208.66 22474.42 28653.10 25294.26 21657.39
## 58 59 60 61 62 63 64 65
## 25987.72 27224.98 27516.73 27921.26 23865.28 40162.90 41974.23 37304.33
## 66 67 68 69 70 71 72 73
## 41436.81 46265.46 56744.80 54793.73 40298.11 37848.12 40812.61 35213.63
## 74 75 76 77 78 79 80 81
## 30746.37 21614.36 24759.17 20757.00 22806.14 17792.02 19771.78 19011.99
## 82 83 84 85 86 87 88 89
## 18056.93 16160.04 17415.19 20959.39 25298.92 26271.26 26295.78 26901.60
## 90 91 92 93 94 95 96 97
## 30953.66 29804.91 30786.58 28885.28 28099.26 28460.89 28860.27 22551.41
## 98 99 100 101 102 103 104 105
## 25513.07 18666.91 17543.30 15618.70 15913.15 16578.55 20972.01 20071.74
## 106 107 108 109 110 111 112 113
## 23520.69 23314.38 24960.82 27786.67 25329.07 21835.51 22106.05 24705.48
## 114 115 116 117 118 119 120 121
## 35377.65 33602.49 35407.44 38352.68 40273.97 38003.31 32916.01 29322.17
## 122 123 124 125 126 127 128 129
## 31368.13 29678.53 30839.17 38304.38 37953.18 37031.33 33950.39 35700.05
## 130 131 132 133 134 135 136 137
## 41002.37 40452.57 31810.05 33052.45 36185.74 32659.93 31075.76 30176.96
## 138 139 140 141 142 143 144 145
## 26794.95 28184.33 27958.04 25685.30 27673.89 26322.99 20038.40 23015.27
## 146 147 148 149 150 151 152 153
## 20880.44 23804.84 24335.27 25897.50 26076.35 27701.22 28978.93 31957.39
## 154 155 156 157 158 159 160 161
## 27507.78 26783.79 24382.34 30172.06 41541.84 39901.64 37313.25 42244.62
## 162 163 164 165 166 167 168 169
## 43628.24 46983.65 42529.88 37920.17 43249.33 59262.80 61435.75 59877.94
## 170 171 172 173 174 175 176 177
## 56759.85 55187.45 57842.16 56883.54 49312.17 52085.67 55762.01 55797.56
## 178 179 180 181 182 183 184 185
## 62800.13 53470.64 50279.12 41250.57 32939.76 36385.89 46310.41 45776.06
## 186 187 188 189 190 191 192 193
## 51623.40 57266.62 67858.62 73047.92 66854.28 67044.29 73989.37 69742.40
## 194 195 196 197 198 199 200 201
## 65344.52 54778.87 48914.74 50313.81 45986.54 38294.06 44605.00 42863.80
## 202 203 204 205 206 207 208 209
## 42508.18 42978.05 49651.73 58383.50 58019.66 59713.92 61340.38 65064.41
## 210 211 212 213 214 215 216 217
## 74363.26 66586.31 54984.57 49688.87 40844.69 37856.30 40914.81 31111.77
## 218 219 220 221 222 223 224 225
## 47784.63 54975.74 55861.67 78223.85 85586.09 87553.83 95010.71 86121.91
## 226 227 228 229 230 231 232 233
## 80226.65 79815.99 76525.30 75701.65 80354.75 81694.86 76228.89 71526.63
## 234 235 236 237 238 239 240 241
## 77080.12 63964.45 56142.02 48235.25 39995.42 44113.07 46227.67 39794.38
## 242 243 244 245 246 247 248 249
## 33585.91 43686.23 38035.03 41901.01 34301.93 33030.72 36621.25 39395.12
## 250 251 252 253 254 255 256 257
## 30387.04 36219.91 39953.99 45056.73 47726.89 47228.46 57346.91 74534.14
## 258 259 260 261 262 263 264 265
## 74352.47 67781.64 69237.22 65524.96 66945.48 60813.76 50278.39 46376.12
## 266 267 268 269 270 271 272 273
## 46581.82 42757.52 51405.68 47744.79 51818.06 49968.00 53963.31 54254.08
## 274 275 276 277 278 279 280 281
## 60164.91 57841.06 67342.66 61375.08 61581.39 59959.70 65600.84 59442.39
## 282 283 284 285 286 287 288 289
## 56067.12 45804.50 44229.65 61156.63 66588.76 66990.96 64454.30 63557.83
## 290 291 292 293 294 295 296 297
## 67487.93 71328.90 52662.06 42939.44 37056.55 47195.11 50379.52 49509.16
## 298 299 300 301 302 303 304 305
## 73276.91 79117.00 79772.84 84303.60 82534.08 77652.35 81057.78 56401.17
## 306 307 308 309 310 311 312 313
## 52728.50 52413.95 46314.09 43572.96 47112.49 39796.59 38541.31 33199.16
## 314 315 316 317 318 319 320 321
## 36916.68 36112.52 39876.14 37896.27 63225.99 61105.39 62700.22 70556.73
## 322 323 324 325 326 327 328 329
## 72901.79 97932.29 96338.68 72596.80 71415.93 69802.34 61896.55 59069.73
## 330 331 332 333 334 335 336 337
## 29550.15 33172.43 33604.18 35883.04 35235.55 40900.47 41957.03 37288.73
## 338 339 340 341 342 343 344 345
## 36517.06 36641.48 32043.30 37936.11 38587.77 38841.65 39701.95 41456.27
## 346 347 348 349 350 351 352 353
## 43246.02 43002.08 36071.58 26806.32 32056.41 30937.57 30534.88 28193.50
## 354 355 356 357 358 359 360 361
## 32789.60 36477.46 40862.10 39083.14 40321.13 42421.76 49686.18 50227.02
## 362 363 364 365 366 367 368 369
## 50425.12 52844.71 50372.65 49827.03 42602.32 39848.69 36093.28 33911.25
## 370 371 372 373 374 375 376 377
## 30038.78 37197.77 39444.28 47190.33 41268.59 40725.54 39288.71 38829.75
## 378 379 380 381 382 383 384 385
## 29858.94 34375.73 27550.29 35624.27 45774.59 49276.99 47580.89 49537.36
## 386 387 388 389 390 391 392 393
## 55647.51 64965.24 58296.58 52862.61 52596.11 59845.21 60359.33 68874.74
## 394 395 396 397 398 399 400 401
## 58173.02 59712.20 59280.09 58773.07 57286.72 56070.19 43062.88 51496.76
## 402 403 404 405 406 407 408 409
## 50514.85 49499.36 55787.50 48461.79 47785.12 46141.37 41895.01 40746.38
## 410 411 412 413 414 415 416 417
## 38844.46 33042.98 40776.66 43651.29 38417.74 33592.78 48201.66 51978.53
## 418 419 420 421 422 423 424 425
## 55838.38 48440.58 44828.85 43528.21 47051.32 35675.02 35409.00 29769.21
## 426 427 428 429 430 431 432 433
## 35260.31 43440.81 50211.21 47033.79 44154.26 41133.75 41023.91 37563.94
## 434 435 436 437 438 439 440 441
## 33770.53 31053.42 32601.67 34418.39 32458.25 37238.47 43334.41 40144.12
## 442 443 444 445 446 447 448 449
## 39855.31 42808.64 40732.04 44651.10 39981.94 31172.57 30030.56 41186.95
## 450 451 452 453 454 455 456 457
## 40874.11 46425.52 42138.71 42482.19 44074.09 47726.65 37779.69 42543.36
## 458 459 460 461 462 463 464 465
## 38047.41 45482.84 48940.73 51515.03 48302.31 50601.77 50799.74 52515.94
## 466 467 468 469 470 471 472 473
## 52022.41 54914.20 52289.65 57251.67 50630.57 48283.67 46894.78 43578.72
## 474 475 476 477 478 479 480 481
## 47268.54 54600.14 49125.47 50798.64 45679.47 44087.08 46870.02 36471.09
## 482 483 484 485 486 487 488 489
## 30146.28 31982.62 34631.69 36095.36 37044.17 30797.21 43106.27 49648.91
## 490 491 492 493 494 495 496 497
## 56358.63 51163.45 55911.19 63410.12 67154.25 53653.17 44397.34 42457.18
## 498 499 500 501 502 503 504 505
## 42774.68 43551.87 38135.92 40492.50 45700.43 51281.75 51978.89 52087.50
## 506 507 508 509 510 511 512 513
## 45900.61 47216.94 43542.19 46249.00 45920.59 39747.31 40858.42 40048.13
## 514 515 516 517 518 519 520 521
## 41134.24 43727.66 36694.19 32056.90 55524.80 63541.41 67129.85 60625.83
## 522 523 524 525 526 527 528 529
## 61941.67 75287.43 82140.94 57533.49 52494.12 49246.83 53548.48 53063.53
## 530 531 532 533 534 535 536 537
## 43420.46 48324.13 60761.04 55378.68 58716.32 62633.66 59738.31 54856.83
## 538 539 540 541 542 543 544 545
## 48434.58 47114.21 54911.14 54665.97 47357.42 49541.77 49371.50 50053.07
## 546 547 548 549 550 551 552 553
## 40867.25 32847.21 37112.33 45085.54 44886.09 46561.84 40677.49 49531.23
## 554 555 556 557 558 559 560 561
## 50666.12 40625.88 49998.28 57722.15 57098.31 60632.45 56474.47 68027.18
## 562 563 564 565 566 567 568 569
## 84420.91 74687.01 63452.65 67793.78 65951.32 67116.98 58835.96 43107.99
## 570 571 572 573 574 575 576 577
## 49992.76 55793.51 56955.38 58994.22 59629.82 56807.05 68837.23 58398.45
## 578 579 580 581 582 583 584 585
## 52232.40 59699.58 61176.98 54393.09 60548.85 56203.19 53300.00 66630.19
## 586 587 588 589 590 591 592 593
## 52316.37 59530.78 58639.21 52472.42 51789.87 52061.03 42941.03 45688.42
## 594 595 596 597 598 599 600 601
## 40409.88 44580.61 53188.94 46638.09 52393.48 54729.22 60298.04 56524.12
## 602 603 604 605 606 607 608 609
## 61252.73 52970.49 54817.24 55579.84 57847.19 58411.08 57959.96 52240.37
## 610 611 612 613 614 615 616 617
## 59177.12 57271.52 54420.06 51184.42 44271.82 55578.50 59397.16 50492.91
## 618 619 620 621 622 623 624 625
## 60710.91 64821.81 58426.77 80261.09 65647.06 58112.09 60079.59 55515.12
## 626
## 46021.48
##
## $shapiro.test
## [1] 0
##
## $levenes.test
## [1] 0
##
## $autcorr
## [1] "No autocorrelation evidence"
##
## $post_sums
## [1] "Post-Est Warning"
##
## $adjr_sq
## [1] 0.8295
##
## $fstat.bootstrap
##
## ORDINARY NONPARAMETRIC BOOTSTRAP
##
##
## Call:
## boot::boot(data = x, statistic = f.stat, R = Reps, formula = depvar ~
## ., parallel = parr)
##
##
## Bootstrap Statistics :
## original bias std. error
## t1* 7.836356 0.5379039 3.589053
## t2* 1727.056119 21.3909327 210.529677
## WARNING: All values of t3* are NA
##
## $itsa.plot
##
## $booted.ints
## Parameter Lower CI Median F-value Upper CI
## 1 interrupt_var 3.296136 7.885637 14.8304
## 2 lag_depvar 1421.484451 1735.232309 2112.6924Ahora con las tendencias descompuestas
require(zoo)
require(scales)
Gastos_casa %>%
dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>%
dplyr::mutate(fecha2=strftime(fecha, format = "%Y-W%V")) %>%
dplyr::mutate(gastador=ifelse(gastador=="Andrés",1,0)) %>%
dplyr::mutate(treat=ifelse(fecha2>"2019-W26",1,0)) %>%
dplyr::mutate(gasto= dplyr::case_when(gasto=="Gas"~"Gas/Bencina",
gasto=="aspiradora"~"electrodomésticos/mantención casa",
gasto=="Plata fiestas patrias basureros"~"donaciones/regalos",
gasto=="Tina"~"electrodomésticos/mantención casa",
gasto=="Nexium"~"Farmacia",
gasto=="donaciones"~"donaciones/regalos",
gasto=="Regalo chocolates"~"donaciones/regalos",
gasto=="filtro piscina msp"~"electrodomésticos/mantención casa",
gasto=="Chromecast"~"electrodomésticos/mantención casa",
gasto=="Muebles ratan"~"electrodomésticos/mantención casa",
gasto=="Vacuna Influenza"~"Farmacia",
gasto=="Easy"~"electrodomésticos/mantención casa",
gasto=="Sopapo"~"electrodomésticos/mantención casa",
gasto=="filtro agua"~"electrodomésticos/mantención casa",
gasto=="ropa tami"~"donaciones/regalos",
gasto=="yaz"~"Farmacia",
gasto=="Yaz"~"Farmacia",
gasto=="Remedio"~"Farmacia",
gasto=="Entel"~"VTR",
gasto=="Kerosen"~"Gas/Bencina",
gasto=="Parafina"~"Gas/Bencina",
gasto=="Plata basurero"~"donaciones/regalos",
gasto=="Matri Andrés Kogan"~"donaciones/regalos",
gasto=="Wild Protein"~"Comida",
gasto=="Granola Wild Foods"~"Comida",
gasto=="uber"~"Transporte",
gasto=="Uber Reñaca"~"Transporte",
gasto=="filtro piscina mspa"~"electrodomésticos/mantención casa",
gasto=="Limpieza Alfombra"~"electrodomésticos/mantención casa",
gasto=="Aspiradora"~"electrodomésticos/mantención casa",
gasto=="Limpieza alfombras"~"electrodomésticos/mantención casa",
gasto=="Pila estufa"~"electrodomésticos/mantención casa",
gasto=="Reloj"~"electrodomésticos/mantención casa",
gasto=="Arreglo"~"electrodomésticos/mantención casa",
gasto=="Pan Pepperino"~"Comida",
gasto=="Cookidoo"~"Comida",
gasto=="remedios"~"Farmacia",
gasto=="Bendina Reñaca"~"Gas/Bencina",
gasto=="Bencina Reñaca"~"Gas/Bencina",
gasto=="Vacunas Influenza"~"Farmacia",
gasto=="Remedios"~"Farmacia",
gasto=="Plata fiestas patrias basureros"~"donaciones/regalos",
T~gasto)) %>%
dplyr::group_by(gastador, fecha,gasto, .drop=F) %>%
#dplyr::mutate(fecha_simp=week(parse_date(fecha))) %>%
# dplyr::mutate(fecha_simp=tsibble::yearweek(fecha)) %>%#después de diosi. Junio 24, 2019
dplyr::summarise(monto=sum(monto)) %>%
dplyr::mutate(gastador_nombre=plyr::revalue(as.character(gastador), c("0" = "Tami", "1"="Andrés"))) %>%
ggplot2::ggplot(aes(x = fecha, y = monto, color=as.factor(gastador_nombre))) +
#stat_summary(geom = "line", fun.y = median, size = 1, alpha=0.5, aes(color="blue")) +
geom_line(size=1) +
facet_grid(gasto~.)+
#geom_text(aes(x = fech_ing_qrt, y = perc_dup-0.05, label = paste0(n)), vjust = -1,hjust = 0, angle=45, size=3) +
geom_vline(xintercept = as.Date("2019-06-24"),linetype = "dashed") +
labs(y="Gastos (en miles)",x="Semanas y Meses", subtitle="Interlineado, incorporación de la Diosi; Azul= Tami; Rojo= Andrés") +
ggtitle( "Figura 6. Gastos Semanales por Gastador e ítem (media)") +
scale_y_continuous(labels = f <- function(x) paste0(x/1000)) +
scale_color_manual(name = "Gastador", values= c("blue", "red"), labels = c("Tami", "Andrés")) +
scale_x_yearweek(breaks = "1 month", minor_breaks = "1 week", labels=date_format("%m/%y")) +
guides(color = F)+
sjPlot::theme_sjplot2() +
theme(axis.text.x = element_text(vjust = 0.5,angle = 35)) +
theme(
panel.border = element_blank(),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
axis.line = element_line(colour = "black")
)
autoplot(forecast::mstl(Gastos_casa$monto, lambda = "auto",iterate=5000000,start =
lubridate::decimal_date(as.Date("2019-03-03"))))
# scale_x_continuous(breaks = seq(0,400,by=30))
msts <- forecast::msts(Gastos_casa$monto,seasonal.periods = c(7,30.5,365.25),start =
lubridate::decimal_date(as.Date("2019-03-03")))
#tbats <- forecast::tbats(msts,use.trend = FALSE)
#plot(tbats, main="Multiple Season Decomposition")library(bsts)
library(CausalImpact)
ts_week_covid<-
Gastos_casa %>%
dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>%
dplyr::mutate(fecha_week=strftime(fecha, format = "%Y-W%V")) %>%
dplyr::mutate(day=as.Date(as.character(lubridate::floor_date(fecha, "day"))))%>%
dplyr::group_by(fecha_week)%>%
dplyr::summarise(gasto_total=sum(monto,na.rm=T)/1000,min_day=min(day))%>%
dplyr::ungroup() %>%
dplyr::mutate(covid=dplyr::case_when(min_day>=as.Date("2020-03-17")~1,TRUE~0))%>%
dplyr::mutate(covid=as.factor(covid))%>%
data.frame()
ts_week_covid$gasto_total_na<-ts_week_covid$gasto_total
post_resp<-ts_week_covid$gasto_total[which(ts_week_covid$covid==1)]
ts_week_covid$gasto_total_na[which(ts_week_covid$covid==1)]<-NA
ts_week_covid$gasto_total[which(ts_week_covid$covid==0)]## [1] 98.357 4.780 56.784 50.506 64.483 67.248 49.299 35.786 58.503
## [10] 64.083 20.148 73.476 127.004 81.551 69.599 134.446 58.936 26.145
## [19] 129.927 104.989 130.860 81.893 95.697 64.579 303.471 151.106 49.275
## [28] 76.293 33.940 83.071 119.512 20.942 58.055 71.728 44.090 33.740
## [37] 59.264 77.410 60.831 63.376 48.754 235.284 29.604 115.143 72.419
## [46] 5.980 80.063 149.178 69.918 107.601 72.724 63.203 99.681 130.309
## [55] 195.898 112.066# Model 1
ssd <- list()
# Local trend, weekly-seasonal #https://qastack.mx/stats/209426/predictions-from-bsts-model-in-r-are-failing-completely - PUSE UN GENERALIZED LOCAL TREND
ssd <- AddLocalLevel(ssd, ts_week_covid$gasto_total_na) #AddSemilocalLinearTrend #AddLocalLevel
# Add weekly seasonal
ssd <- AddSeasonal(ssd, ts_week_covid$gasto_total_na,nseasons=5, season.duration = 52) #weeks OJO, ESTOS NO SON WEEKS VERDADEROS. PORQUE TENGO MAS DE EUN AÑO
ssd <- AddSeasonal(ssd, ts_week_covid$gasto_total_na, nseasons = 12, season.duration =4) #years
# For example, to add a day-of-week component to data with daily granularity, use model.args = list(nseasons = 7, season.duration = 1). To add a day-of-week component to data with hourly granularity, set model.args = list(nseasons = 7, season.duration = 24).
model1d1 <- bsts(ts_week_covid$gasto_total_na,
state.specification = ssd, #A list with elements created by AddLocalLinearTrend, AddSeasonal, and similar functions for adding components of state. See the help page for state.specification.
family ="student", #A Bayesian Analysis of Time-Series Event Count Data. POISSON NO SE PUEDE OCUPAR
niter = 20000,
#burn = 200, #http://finzi.psych.upenn.edu/library/bsts/html/SuggestBurn.html Suggest the size of an MCMC burn in sample as a proportion of the total run.
seed= 2125)## =-=-=-=-= Iteration 0 Mon Oct 02 01:11:43 2023
## =-=-=-=-=
## =-=-=-=-= Iteration 2000 Mon Oct 02 01:11:54 2023
## =-=-=-=-=
## =-=-=-=-= Iteration 4000 Mon Oct 02 01:12:05 2023
## =-=-=-=-=
## =-=-=-=-= Iteration 6000 Mon Oct 02 01:12:16 2023
## =-=-=-=-=
## =-=-=-=-= Iteration 8000 Mon Oct 02 01:12:27 2023
## =-=-=-=-=
## =-=-=-=-= Iteration 10000 Mon Oct 02 01:12:38 2023
## =-=-=-=-=
## =-=-=-=-= Iteration 12000 Mon Oct 02 01:12:49 2023
## =-=-=-=-=
## =-=-=-=-= Iteration 14000 Mon Oct 02 01:13:00 2023
## =-=-=-=-=
## =-=-=-=-= Iteration 16000 Mon Oct 02 01:13:10 2023
## =-=-=-=-=
## =-=-=-=-= Iteration 18000 Mon Oct 02 01:13:21 2023
## =-=-=-=-=#,
# dynamic.regression=T)
#plot(model1d1, main = "Model 1")
#plot(model1d1, "components")
impact2d1 <- CausalImpact(bsts.model = model1d1,
post.period.response = post_resp)
plot(impact2d1)+
xlab("Date")+
ylab("Monto Semanal (En miles)")
burn1d1 <- SuggestBurn(0.1, model1d1)corpus <- Corpus(VectorSource(Gastos_casa$obs)) # formato de texto
d <- tm_map(corpus, tolower)
d <- tm_map(d, stripWhitespace)
d <- tm_map(d, removePunctuation)
d <- tm_map(d, removeNumbers)
d <- tm_map(d, removeWords, stopwords("spanish"))
d <- tm_map(d, removeWords, "menos")
tdm <- TermDocumentMatrix(d)
m <- as.matrix(tdm) #lo vuelve una matriz
v <- sort(rowSums(m),decreasing=TRUE) #lo ordena y suma
df <- data.frame(word = names(v),freq=v) # lo nombra y le da formato de data.frame
#findFreqTerms(tdm)
#require(devtools)
#install_github("lchiffon/wordcloud2")
#wordcloud2::wordcloud2(v, size=1.2)
wordcloud(words = df$word, freq = df$freq,
max.words=100, random.order=FALSE, rot.per=0.35,
colors=brewer.pal(8, "Dark2"), main="Figura 7. Nube de Palabras, Observaciones")
fit_month_gasto <- Gastos_casa %>%
dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>%
dplyr::mutate(fecha_month=strftime(fecha, format = "%Y-%m")) %>%
dplyr::mutate(day=as.Date(as.character(lubridate::floor_date(fecha, "day"))))%>%
dplyr::mutate(gasto2= dplyr::case_when(gasto=="Gas"~"Gas/Bencina",
gasto=="aspiradora"~"electrodomésticos/mantención casa",
gasto=="Plata fiestas patrias basureros"~"donaciones/regalos",
gasto=="Tina"~"Electrodomésticos/ Mantención casa",
gasto=="Nexium"~"Farmacia",
gasto=="donaciones"~"donaciones/regalos",
gasto=="Regalo chocolates"~"donaciones/regalos",
gasto=="filtro piscina msp"~"Electrodomésticos/ Mantención casa",
gasto=="Chromecast"~"Electrodomésticos/ Mantención casa",
gasto=="Muebles ratan"~"Electrodomésticos/ Mantención casa",
gasto=="Vacuna Influenza"~"Farmacia",
gasto=="Easy"~"Electrodomésticos/ Mantención casa",
gasto=="Sopapo"~"Electrodomésticos/ Mantención casa",
gasto=="filtro agua"~"Electrodomésticos/ Mantención casa",
gasto=="ropa tami"~"donaciones/regalos",
gasto=="yaz"~"Farmacia",
gasto=="Yaz"~"Farmacia",
gasto=="Remedio"~"Farmacia",
gasto=="Entel"~"VTR",
gasto=="Kerosen"~"Gas/Bencina",
gasto=="Parafina"~"Gas/Bencina",
gasto=="Plata basurero"~"donaciones/regalos",
gasto=="Matri Andrés Kogan"~"donaciones/regalos",
gasto=="Wild Protein"~"Comida",
gasto=="Granola Wild Foods"~"Comida",
gasto=="uber"~"Otros",
gasto=="Uber Reñaca"~"Otros",
gasto=="filtro piscina mspa"~"Electrodomésticos/ Mantención casa",
gasto=="Limpieza Alfombra"~"Electrodomésticos/ Mantención casa",
gasto=="Aspiradora"~"Electrodomésticos/ Mantención casa",
gasto=="Limpieza alfombras"~"Electrodomésticos/ Mantención casa",
gasto=="Pila estufa"~"Electrodomésticos/ Mantención casa",
gasto=="Reloj"~"Electrodomésticos/ Mantención casa",
gasto=="Arreglo"~"Electrodomésticos/ Mantención casa",
gasto=="Pan Pepperino"~"Comida",
gasto=="Cookidoo"~"Comida",
gasto=="remedios"~"Farmacia",
gasto=="Bendina Reñaca"~"Gas/Bencina",
gasto=="Bencina Reñaca"~"Gas/Bencina",
gasto=="Vacunas Influenza"~"Farmacia",
gasto=="Remedios"~"Farmacia",
gasto=="Plata fiestas patrias basureros"~"donaciones/regalos",
T~gasto)) %>%
dplyr::mutate(fecha_month=factor(fecha_month, levels=format(seq(from = as.Date("2019-03-03"), to = as.Date(substr(Sys.time(),1,10)), by = "1 month"),"%Y-%m")))%>%
dplyr::mutate(gasto2=factor(gasto2, levels=c("Agua", "Comida", "Comunicaciones","Electricidad", "Enceres", "Farmacia", "Gas/Bencina", "Diosi", "donaciones/regalos", "Electrodomésticos/ Mantención casa", "VTR", "Netflix", "Otros")))%>%
dplyr::group_by(fecha_month, gasto2, .drop=F)%>%
dplyr::summarise(gasto_total=sum(monto, na.rm = T)/1000)%>%
data.frame() %>% na.omit()
fit_month_gasto_23<-
fit_month_gasto %>%
#dplyr::filter()
dplyr::filter(grepl("2023",fecha_month)) %>%
#sacar el ultimo mes
dplyr::filter(as.character(format(as.Date(substr(Sys.time(),1,10)),"%Y-%m"))!=fecha_month) %>%
dplyr::group_by(gasto2) %>%
dplyr::summarise(gasto_prom=mean(gasto_total, na.rm=T)) %>%
data.frame()%>% ungroup()
fit_month_gasto_22<-
fit_month_gasto %>%
#dplyr::filter()
dplyr::filter(grepl("2022",fecha_month)) %>%
#sacar el ultimo mes
dplyr::filter(as.character(format(as.Date(substr(Sys.time(),1,10)),"%Y-%m"))!=fecha_month) %>%
dplyr::group_by(gasto2) %>%
dplyr::summarise(gasto_prom=mean(gasto_total, na.rm=T)) %>%
data.frame()%>% ungroup()
fit_month_gasto_21<-
fit_month_gasto %>%
#dplyr::filter()
dplyr::filter(grepl("2021|2022",fecha_month)) %>%
#sacar el ultimo mes
dplyr::filter(as.character(format(as.Date(substr(Sys.time(),1,10)),"%Y-%m"))!=fecha_month) %>%
dplyr::group_by(gasto2) %>%
dplyr::summarise(gasto_prom=mean(gasto_total, na.rm=T)) %>%
data.frame()%>% ungroup()
fit_month_gasto_20<-
fit_month_gasto %>%
#dplyr::filter()
dplyr::filter(grepl("202",fecha_month)) %>%
#sacar el ultimo mes
dplyr::filter(as.character(format(as.Date(substr(Sys.time(),1,10)),"%Y-%m"))!=fecha_month) %>%
dplyr::group_by(gasto2) %>%
dplyr::summarise(gasto_prom=mean(gasto_total, na.rm=T)) %>%
data.frame() %>% ungroup()
fit_month_gasto_23 %>%
dplyr::right_join(fit_month_gasto_22,by="gasto2") %>%
dplyr::right_join(fit_month_gasto_21,by="gasto2") %>%
dplyr::right_join(fit_month_gasto_20,by="gasto2") %>%
janitor::adorn_totals() %>%
#dplyr::select(-3)%>%
knitr::kable(format = "markdown", size=12, col.names= c("Item","2023","2022","2021","2020"))| Item | 2023 | 2022 | 2021 | 2020 |
|---|---|---|---|---|
| Agua | 5.251556 | 5.410333 | 5.629750 | 6.7029111 |
| Comida | 365.239889 | 310.278417 | 314.087500 | 345.3436222 |
| Comunicaciones | 0.000000 | 0.000000 | 0.000000 | 0.0000000 |
| Electricidad | 35.509667 | 47.072333 | 38.297667 | 33.0219111 |
| Enceres | 22.524111 | 20.086417 | 17.443792 | 24.2113778 |
| Farmacia | 2.220000 | 1.831667 | 7.913875 | 8.4078667 |
| Gas/Bencina | 38.292444 | 44.325000 | 28.954333 | 27.7030222 |
| Diosi | 15.216778 | 31.180667 | 41.934250 | 35.3073111 |
| donaciones/regalos | 0.000000 | 0.000000 | 7.170083 | 6.1048667 |
| Electrodomésticos/ Mantención casa | 0.000000 | 3.944000 | 30.269500 | 18.4326222 |
| VTR | 14.661111 | 25.156667 | 22.121792 | 19.8275111 |
| Netflix | 5.148889 | 7.151583 | 7.090167 | 7.0104667 |
| Otros | 0.000000 | 3.151083 | 1.575542 | 0.8402889 |
| Total | 504.064444 | 499.588167 | 522.488250 | 532.9137778 |
## Joining with `by = join_by(word)`
2. UF Proyectada
Saqué la UF proyectada
#options(max.print=5000)
uf18 <-rvest::read_html("https://www.sii.cl/valores_y_fechas/uf/uf2018.htm")%>% rvest::html_nodes("table")
uf19 <-rvest::read_html("https://www.sii.cl/valores_y_fechas/uf/uf2019.htm")%>% rvest::html_nodes("table")
uf20 <-rvest::read_html("https://www.sii.cl/valores_y_fechas/uf/uf2020.htm")%>% rvest::html_nodes("table")
uf21 <-rvest::read_html("https://www.sii.cl/valores_y_fechas/uf/uf2021.htm")%>% rvest::html_nodes("table")
uf22 <-rvest::read_html("https://www.sii.cl/valores_y_fechas/uf/uf2022.htm")%>% rvest::html_nodes("table")
tryCatch(uf23 <-rvest::read_html("https://www.sii.cl/valores_y_fechas/uf/uf2023.htm")%>% rvest::html_nodes("table"),
error = function(c) {
uf23b <<- cbind.data.frame(Día=NA, variable=NA, value=NA)
}
)
tryCatch(uf23 <-uf23[[length(uf23)]] %>% rvest::html_table() %>% data.frame() %>% reshape2::melt(id.vars=1),
error = function(c) {
uf23 <<- cbind.data.frame(Día=NA, variable=NA, value=NA)
}
)
uf_serie<-
bind_rows(
cbind.data.frame(anio= 2018, uf18[[length(uf18)]] %>% rvest::html_table() %>% data.frame() %>% reshape2::melt(id.vars=1)),
cbind.data.frame(anio= 2019, uf19[[length(uf19)]] %>% rvest::html_table() %>% data.frame() %>% reshape2::melt(id.vars=1)),
cbind.data.frame(anio= 2020, uf20[[length(uf20)]] %>% rvest::html_table() %>% data.frame() %>% reshape2::melt(id.vars=1)),
cbind.data.frame(anio= 2021, uf21[[length(uf21)]] %>% rvest::html_table() %>% data.frame() %>% reshape2::melt(id.vars=1)),
cbind.data.frame(anio= 2022, uf22[[length(uf22)]] %>% rvest::html_table() %>% data.frame() %>% reshape2::melt(id.vars=1)),
cbind.data.frame(anio= 2023, uf23)
)
uf_serie_corrected<-
uf_serie %>%
dplyr::mutate(month=plyr::revalue(tolower(.[[3]]),c("ene" = 1, "feb"=2, "mar"=3, "abr"=4, "may"=5, "jun"=6, "jul"=7, "ago"=8, "sep"=9, "oct"=10, "nov"=11, "dic"=12))) %>%
dplyr::mutate(value=stringr::str_trim(value), value= sub("\\.","",value),value= as.numeric(sub("\\,",".",value))) %>%
dplyr::mutate(date=paste0(sprintf("%02d", .[[2]])," ",sprintf("%02d",as.numeric(month)),", ",.[[1]]), date3=lubridate::parse_date_time(date,c("%d %m, %Y"),exact=T),date2=date3) %>%
na.omit()#%>% dplyr::filter(is.na(date3))## Warning: There was 1 warning in `dplyr::mutate()`.
## i In argument: `date3 = lubridate::parse_date_time(date, c("%d %m, %Y"), exact
## = T)`.
## Caused by warning:
## ! 41 failed to parse.#Day of the month as decimal number (1–31), with a leading space for a single-digit number.
#Abbreviated month name in the current locale on this platform. (Also matches full name on input: in some locales there are no abbreviations of names.)
warning(paste0("number of observations:",nrow(uf_serie_corrected),", min uf: ",min(uf_serie_corrected$value),", min date: ",min(uf_serie_corrected $date3 )))## Warning: number of observations:2108, min uf: 26799.01, min date: 2018-01-01#
# uf_proyectado <- readxl::read_excel("uf_proyectado.xlsx") %>% dplyr::arrange(Período) %>%
# dplyr::mutate(Período= as.Date(lubridate::parse_date_time(Período, c("%Y-%m-%d"),exact=T)))
ts_uf_proy<-
ts(data = uf_serie_corrected$value,
start = as.numeric(as.Date("2018-01-01")),
end = as.numeric(as.Date(uf_serie_corrected$date3[length(uf_serie_corrected$date3)])), frequency = 1,
deltat = 1, ts.eps = getOption("ts.eps"))
fit_tbats <- forecast::tbats(ts_uf_proy)
fr_fit_tbats<-forecast::forecast(fit_tbats, h=298)La proyección de la UF a 298 días más 2023-10-09 00:04:58 sería de: 36.311 pesos// Percentil 95% más alto proyectado: 39.184,34
Ahora con un modelo ARIMA automático
arima_optimal_uf = forecast::auto.arima(ts_uf_proy)
autoplotly::autoplotly(forecast::forecast(arima_optimal_uf, h=298), ts.colour = "darkred",
predict.colour = "blue", predict.linetype = "dashed")%>%
plotly::layout(showlegend = F,
yaxis = list(title = "Gastos"),
xaxis = list(
title="Fecha",
ticktext = as.list(seq(from = as.Date("2018-01-01"),
to = as.Date("2018-01-01")+length(fit_tbats$fitted.values)+298, by = 90)),
tickvals = as.list(seq(from = as.numeric(as.Date("2018-01-01")),
to = as.numeric(as.Date("2018-01-01"))+length(fit_tbats$fitted.values)+298, by = 90)),
tickmode = "array",
tickangle = 90
))fr_fit_tbats_uf<-forecast::forecast(arima_optimal_uf, h=298)dplyr::group_by(reshape2::melt(data.frame(fr_fit_tbats)),variable) %>% dplyr::summarise(max=max(value)) %>%
dplyr::right_join(dplyr::group_by(reshape2::melt(data.frame(fr_fit_tbats_uf)),variable) %>% dplyr::summarise(max=max(value)),by="variable") %>%
dplyr::mutate(variable=factor(variable,levels=c("Lo.95","Lo.80","Point.Forecast","Hi.80","Hi.95"))) %>%
dplyr::arrange(variable) %>%
knitr::kable(format="markdown", caption="Tabla. Estimación UF (de aquí a 298 días) según cálculos de gastos mensuales",
col.names= c("Item","UF Proyectada (TBATS)","UF Proyectada (ARIMA)"))## No id variables; using all as measure variables
## No id variables; using all as measure variables| Item | UF Proyectada (TBATS) | UF Proyectada (ARIMA) |
|---|---|---|
| Lo.95 | 36207.50 | 36207.15 |
| Lo.80 | 36208.22 | 36207.99 |
| Point.Forecast | 36311.00 | 36565.98 |
| Hi.80 | 37938.72 | 41299.98 |
| Hi.95 | 38829.72 | 43806.01 |
3. Gastos proyectados
Lo haré en base a 2 cálculos: el gasto semanal y el gasto mensual en base a mis gastos desde marzo de 2019. La primera proyección la hice añadiendo el precio del arriendo mensual y partiendo en 2 (porque es con yo y Tami). No se incluye el último mes.
Gastos_casa_nvo <- readr::read_csv(as.character(path_sec),
col_names = c("Tiempo", "gasto", "fecha", "obs", "monto", "gastador",
"link"),skip=1) %>%
dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>%
dplyr::mutate(fecha_month=strftime(fecha, format = "%Y-%m")) %>%
dplyr::mutate(day=as.Date(as.character(lubridate::floor_date(fecha, "day"))))
Gastos_casa_m <-
Gastos_casa_nvo %>% dplyr::group_by(fecha_month)%>%
dplyr::summarise(gasto_total=(sum(monto)+500000)/1000,fecha=first(fecha))%>%
data.frame()
uf_serie_corrected_m <-
uf_serie_corrected %>% dplyr::mutate(ano_m=paste0(anio,"-",sprintf("%02d",as.numeric(month)))) %>% dplyr::group_by(ano_m)%>%
dplyr::summarise(uf=(mean(value))/1000,fecha=first(date3))%>%
data.frame() %>%
dplyr::filter(fecha>="2019-02-28")
#Error: Error in standardise_path(file) : object 'enlace_gastos' not found
ts_uf_serie_corrected_m<-
ts(data = uf_serie_corrected_m$uf[-length(uf_serie_corrected_m$uf)],
start = 1,
end = nrow(uf_serie_corrected_m),
frequency = 1,
deltat = 1, ts.eps = getOption("ts.eps"))
ts_gastos_casa_m<-
ts(data = Gastos_casa_m$gasto_total[-length(Gastos_casa_m$gasto_total)],
start = 1,
end = nrow(Gastos_casa_m),
frequency = 1,
deltat = 1, ts.eps = getOption("ts.eps"))
fit_tbats_m <- forecast::tbats(ts_gastos_casa_m)
seq_dates<-format(seq(as.Date("2019/03/01"), by = "month", length = dim(Gastos_casa_m)[1]+12), "%m\n'%y")
autplo2t<-
autoplotly::autoplotly(forecast::forecast(fit_tbats_m, h=12), ts.colour = "darkred",
predict.colour = "blue", predict.linetype = "dashed")%>%
plotly::layout(showlegend = F,
yaxis = list(title = "Gastos (en miles)"),
xaxis = list(
title="Fecha",
ticktext = as.list(seq_dates[seq(from = 1, to = (dim(Gastos_casa_m)[1]+12), by = 3)]),
tickvals = as.list(seq(from = 1, to = (dim(Gastos_casa_m)[1]+12), by = 3)),
tickmode = "array"#"array"
))
autplo2tAhora asumiendo un modelo ARIMA, e incluimos como regresor al precio de la UF.
paste0("Optimo pero sin regresor")## [1] "Optimo pero sin regresor"arima_optimal = forecast::auto.arima(ts_gastos_casa_m)
arima_optimal## Series: ts_gastos_casa_m
## ARIMA(1,0,0) with non-zero mean
##
## Coefficients:
## ar1 mean
## 0.2940 1007.0359
## s.e. 0.1341 30.9802
##
## sigma^2 = 27529: log likelihood = -358.2
## AIC=722.4 AICc=722.87 BIC=728.42paste0("Optimo pero con regresor")## [1] "Optimo pero con regresor"arima_optimal2 = forecast::auto.arima(ts_gastos_casa_m, xreg=as.numeric(ts_uf_serie_corrected_m[1:(length(Gastos_casa_m$gasto_total))]))
arima_optimal2## Series: ts_gastos_casa_m
## Regression with ARIMA(1,0,0) errors
##
## Coefficients:
## ar1 intercept xreg
## 0.2745 737.6209 8.7268
## s.e. 0.1357 317.7899 10.2362
##
## sigma^2 = 27708: log likelihood = -357.85
## AIC=723.7 AICc=724.5 BIC=731.73forecast_uf<-
cbind.data.frame(fecha=as.Date(seq(as.numeric(as.Date(uf_serie_corrected$date3[length(uf_serie_corrected$date3)])),(as.numeric(as.Date(uf_serie_corrected$date3[length(uf_serie_corrected$date3)]))+299),by=1), origin = "1970-01-01"),forecast::forecast(fit_tbats, h=300)) %>%
dplyr::mutate(ano_m=stringr::str_extract(fecha,".{7}")) %>%
dplyr::group_by(ano_m)%>%
dplyr::summarise(uf=(mean(`Hi 95`,na.rm=T))/1000,fecha=first(fecha))%>%
data.frame()
autplo2t2<-
autoplotly::autoplotly(forecast::forecast(arima_optimal2,xreg=c(forecast_uf$uf[1],forecast_uf$uf), h=12), ts.colour = "darkred",
predict.colour = "blue", predict.linetype = "dashed")%>%
plotly::layout(showlegend = F,
yaxis = list(title = "Gastos (en miles)"),
xaxis = list(
title="Fecha",
ticktext = as.list(seq_dates[seq(from = 1, to = (dim(Gastos_casa_m)[1]+12), by = 3)]),
tickvals = as.list(seq(from = 1, to = (dim(Gastos_casa_m)[1]+12), by = 3)),
tickmode = "array"#"array"
))
autplo2t2fr_fit_tbats_m<-forecast::forecast(fit_tbats_m, h=12)
fr_fit_tbats_m2<-forecast::forecast(arima_optimal, h=12)
fr_fit_tbats_m3<-forecast::forecast(arima_optimal2, h=12,xreg=c(forecast_uf$uf[1],forecast_uf$uf))
dplyr::right_join(dplyr::group_by(reshape2::melt(data.frame(fr_fit_tbats_m3)),variable) %>% dplyr::summarise(max=max(value)), dplyr::group_by(reshape2::melt(data.frame(fr_fit_tbats_m2)),variable) %>% dplyr::summarise(max=max(value)),by="variable") %>%
dplyr::right_join(dplyr::group_by(reshape2::melt(data.frame(fr_fit_tbats_m)),variable) %>% dplyr::summarise(max=max(value)),by="variable") %>%
dplyr::mutate(variable=factor(variable,levels=c("Lo.95","Lo.80","Point.Forecast","Hi.80","Hi.95"))) %>%
dplyr::arrange(variable) %>%
knitr::kable(format="markdown", caption="Estimación en miles de la plata a gastar en el futuro (de aquí a 12 meses) según cálculos de gastos mensuales",
col.names= c("Item","Modelo ARIMA con regresor (UF)","Modelo ARIMA sin regresor","Modelo TBATS")) ## No id variables; using all as measure variables
## No id variables; using all as measure variables
## No id variables; using all as measure variables| Item | Modelo ARIMA con regresor (UF) | Modelo ARIMA sin regresor | Modelo TBATS |
|---|---|---|---|
| Lo.95 | 737.2595 | 666.8033 | 704.5071 |
| Lo.80 | 854.6978 | 784.5697 | 787.2008 |
| Point.Forecast | 1076.5439 | 1007.0358 | 970.8084 |
| Hi.80 | 1298.3901 | 1229.5019 | 1259.1953 |
| Hi.95 | 1415.8283 | 1347.2684 | 1445.0633 |
4. Gastos mensuales (resumen manual)
path_sec2<- paste0("https://docs.google.com/spreadsheets/d/",Sys.getenv("SUPERSECRET"),"/export?format=csv&id=",Sys.getenv("SUPERSECRET"),"&gid=847461368")
Gastos_casa_mensual_2022 <- readr::read_csv(as.character(path_sec2),
#col_names = c("Tiempo", "gasto", "fecha", "obs", "monto", "gastador","link"),
skip=0)## Rows: 66 Columns: 4
## -- Column specification --------------------------------------------------------
## Delimiter: ","
## chr (1): mes_ano
## dbl (3): n, Tami, Andrés
##
## i Use `spec()` to retrieve the full column specification for this data.
## i Specify the column types or set `show_col_types = FALSE` to quiet this message.head(Gastos_casa_mensual_2022,5) %>%
knitr::kable("markdown",caption="Resumen mensual, primeras 5 observaciones")| n | mes_ano | Tami | Andrés |
|---|---|---|---|
| 1 | marzo_2019 | 175533 | 68268 |
| 2 | abril_2019 | 152640 | 55031 |
| 3 | mayo_2019 | 152985 | 192219 |
| 4 | junio_2019 | 291067 | 84961 |
| 5 | julio_2019 | 241389 | 205893 |
(
Gastos_casa_mensual_2022 %>%
reshape2::melt(id.var=c("n","mes_ano")) %>%
dplyr::mutate(gastador=as.factor(variable)) %>%
dplyr::select(-variable) %>%
ggplot2::ggplot(aes(x = n, y = value, color=gastador)) +
scale_color_manual(name="Gastador", values=c("red", "blue"))+
geom_line(size=1) +
#geom_vline(xintercept = as.Date("2019-06-24"),linetype = "dashed") +
labs(y="Gastos (en miles)",x="Meses", subtitle="Azul= Tami; Rojo= Andrés") +
ggtitle( "Gastos Mensuales (total manual)") +
scale_y_continuous(labels = f <- function(x) paste0(x/1000)) +
# scale_color_manual(name = "Gastador", values= c("blue", "red"), labels = c("Tami", "Andrés")) +
# scale_x_yearweek(breaks = "1 month", minor_breaks = "1 week", labels=date_format("%m/%y")) +
# guides(color = F)+
sjPlot::theme_sjplot2() +
theme(axis.text.x = element_text(vjust = 0.5,angle = 35)) +
theme(
panel.border = element_blank(),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
axis.line = element_line(colour = "black")
)
) %>% ggplotly()Session Info
Sys.getenv("R_LIBS_USER")## [1] "D:\\a\\_temp\\Library"sessionInfo()## R version 4.1.2 (2021-11-01)
## Platform: x86_64-w64-mingw32/x64 (64-bit)
## Running under: Windows Server x64 (build 20348)
##
## Matrix products: default
##
## locale:
## [1] LC_COLLATE=Spanish_Chile.1252 LC_CTYPE=Spanish_Chile.1252
## [3] LC_MONETARY=Spanish_Chile.1252 LC_NUMERIC=C
## [5] LC_TIME=Spanish_Chile.1252
##
## attached base packages:
## [1] grid stats graphics grDevices utils datasets methods
## [8] base
##
## other attached packages:
## [1] CausalImpact_1.3.0 bsts_0.9.9 BoomSpikeSlab_1.2.5
## [4] Boom_0.9.11 scales_1.2.1 ggiraph_0.8.7
## [7] tidytext_0.4.1 DT_0.29 autoplotly_0.1.4
## [10] rvest_1.0.3 plotly_4.10.2 xts_0.13.1
## [13] forecast_8.21.1 wordcloud_2.6 RColorBrewer_1.1-3
## [16] SnowballC_0.7.1 tm_0.7-11 NLP_0.2-1
## [19] tsibble_1.1.3 lubridate_1.9.3 forcats_1.0.0
## [22] dplyr_1.1.3 purrr_1.0.1 tidyr_1.3.0
## [25] tibble_3.2.1 ggplot2_3.4.3 tidyverse_2.0.0
## [28] sjPlot_2.8.15 lattice_0.20-45 gridExtra_2.3
## [31] plotrix_3.8-2 sparklyr_1.8.3 httr_1.4.7
## [34] readxl_1.4.3 zoo_1.8-12 stringr_1.5.0
## [37] stringi_1.7.12 data.table_1.14.8 reshape2_1.4.4
## [40] fUnitRoots_4021.80 plyr_1.8.8 readr_2.1.4
##
## loaded via a namespace (and not attached):
## [1] uuid_1.1-0 backports_1.4.1 systemfonts_1.0.4
## [4] selectr_0.4-2 lazyeval_0.2.2 splines_4.1.2
## [7] crosstalk_1.2.0 digest_0.6.31 htmltools_0.5.5
## [10] fansi_1.0.4 ggfortify_0.4.16 magrittr_2.0.3
## [13] tzdb_0.4.0 modelr_0.1.11 vroom_1.6.3
## [16] timechange_0.2.0 anytime_0.3.9 tseries_0.10-54
## [19] colorspace_2.1-0 xfun_0.39 crayon_1.5.2
## [22] jsonlite_1.8.4 lme4_1.1-34 glue_1.6.2
## [25] gtable_0.3.4 emmeans_1.8.8 sjstats_0.18.2
## [28] sjmisc_2.8.9 car_3.1-2 quantmod_0.4.25
## [31] abind_1.4-5 mvtnorm_1.2-3 DBI_1.1.3
## [34] ggeffects_1.3.1 Rcpp_1.0.10 viridisLite_0.4.2
## [37] xtable_1.8-4 performance_0.10.5 bit_4.0.5
## [40] htmlwidgets_1.6.2 timeSeries_4031.107 gplots_3.1.3
## [43] ellipsis_0.3.2 spatial_7.3-14 pkgconfig_2.0.3
## [46] farver_2.1.1 nnet_7.3-16 sass_0.4.5
## [49] dbplyr_2.3.4 janitor_2.2.0 utf8_1.2.3
## [52] tidyselect_1.2.0 labeling_0.4.3 rlang_1.1.0
## [55] munsell_0.5.0 cellranger_1.1.0 tools_4.1.2
## [58] cachem_1.0.7 cli_3.6.1 generics_0.1.3
## [61] sjlabelled_1.2.0 broom_1.0.5 evaluate_0.20
## [64] fastmap_1.1.1 yaml_2.3.7 knitr_1.44
## [67] bit64_4.0.5 caTools_1.18.2 nlme_3.1-153
## [70] slam_0.1-50 xml2_1.3.3 tokenizers_0.3.0
## [73] compiler_4.1.2 rstudioapi_0.14 curl_5.0.2
## [76] bslib_0.4.2 fBasics_4022.94 Matrix_1.6-1.1
## [79] its.analysis_1.6.0 nloptr_2.0.3 urca_1.3-3
## [82] vctrs_0.6.1 pillar_1.9.0 lifecycle_1.0.3
## [85] lmtest_0.9-40 jquerylib_0.1.4 estimability_1.4.1
## [88] bitops_1.0-7 insight_0.19.5 R6_2.5.1
## [91] KernSmooth_2.23-20 janeaustenr_1.0.0 codetools_0.2-18
## [94] assertthat_0.2.1 boot_1.3-28 MASS_7.3-54
## [97] gtools_3.9.4 withr_2.5.1 fracdiff_1.5-2
## [100] bayestestR_0.13.1 parallel_4.1.2 hms_1.1.3
## [103] quadprog_1.5-8 timeDate_4022.108 minqa_1.2.6
## [106] snakecase_0.11.1 rmarkdown_2.25 carData_3.0-5
## [109] TTR_0.24.3 base64enc_0.1-3#save.image("__analisis.RData")
sesion_info <- devtools::session_info()
dplyr::select(
tibble::as_tibble(sesion_info$packages),
c(package, loadedversion, source)
) %>%
DT::datatable(filter = 'top', colnames = c('Row number' =1,'Variable' = 2, 'Percentage'= 3),
caption = htmltools::tags$caption(
style = 'caption-side: top; text-align: left;',
'', htmltools::em('Packages')),
options=list(
initComplete = htmlwidgets::JS(
"function(settings, json) {",
"$(this.api().tables().body()).css({
'font-family': 'Helvetica Neue',
'font-size': '50%',
'code-inline-font-size': '15%',
'white-space': 'nowrap',
'line-height': '0.75em',
'min-height': '0.5em'
});",#;
"}")))